Semantic Web Recommender systems Cai-Nicolas Ziegler Institut fur Informatik, Group DBIs, Universitat Freiburg, Germany ziegler@informatik. uni-freiburg de Abstract. Research on recommendersystems has primarily addressed centralized scenarios and largely ignored open, decentralized systems where remote informa- tion distribution prevails. The absence of superordinate authorities having full access and control introduces some serious issues requiring novel approaches and methods. Hence, our primary objective targets the successful deployment and inte gration of recommender system facilities for Semantic Web applications, making use of novel technologies and concepts and incorporating them into one coherent 1 Introduction Automated recommender systems intend to provide people with recommendations of products they might appreciate, taking into account their past product ratings profile and history of purchase or interest. Most successful systems apply so-called social filtering techniques [9], dubbed collaborative filtering [6]. These systems identify similar users and make recommendations based upon products people utterly fancy Unfortunately, common collaborative filtering methods fail when transplanted into decentralized scenarios. Analyzing the issues specific to these domains, we believe that two novel approaches may alleviate the prevailing problems, namely trust networks, along with trust propagation mechanisms, and taxonomy-driven profile generation and filtering. One aspect of our work hence addresses the conception of suitable components specifically tailored to suit our decentralized setting, while another regards the seamless integration of these latter building bricks into one single, unified framework. Empirical analysis and performance evaluations are conducted at all stages 2 Research issues Deploying recommender systems into the Semantic Web implies diverse, multi-faceted issues, some of them being inherent to decentralized systems in general, others being cific. Hereby, our devised Semantic Web recommender system performs all recom- mendation computations locally for one given user. Its principal difference from generic, centralized approaches refers to information storage, supposing all user and rating data distributed throughout the Semantic Web. Hence its decentralized nature. We thus come to identify several research issues: et al. (Eds ) EDBT 2004 Workshops, LNCS 3268, pp. 78-89, 2004
Semantic Web Recommender Systems Cai-Nicolas Ziegler Institut f¨ur Informatik, Group DBIS, Universit¨at Freiburg, Germany cziegler@informatik.uni-freiburg.de Abstract. Research on recommender systems has primarily addressed centralized scenarios and largely ignored open, decentralized systems where remote information distribution prevails. The absence of superordinate authorities having full access and control introduces some serious issues requiring novel approaches and methods. Hence, our primary objective targets the successful deployment and integration of recommender system facilities for Semantic Web applications, making use of novel technologies and concepts and incorporating them into one coherent framework. 1 Introduction Automated recommender systems intend to provide people with recommendations of products they might appreciate, taking into account their past product ratings profile and history of purchase or interest. Most successful systems apply so-called social filtering techniques [9], dubbed collaborative filtering [6]. These systems identify similar users and make recommendations based upon products people utterly fancy. Unfortunately, common collaborative filtering methods fail when transplanted into decentralized scenarios. Analyzing the issues specific to these domains, we believe that two novel approaches may alleviate the prevailing problems, namely trust networks, along with trust propagation mechanisms, and taxonomy-driven profile generation and filtering. One aspect of our work hence addresses the conception of suitable components, specifically tailored to suit our decentralized setting, while another regards the seamless integration of these latter building bricks into one single, unified framework. Empirical analysis and performance evaluations are conducted at all stages. 2 Research Issues Deploying recommender systems into the Semantic Web implies diverse, multi-faceted issues, some of them being inherent to decentralized systems in general, others being specific. Hereby, our devised Semantic Web recommender system performs all recommendation computationslocally for one given user. Its principal difference from generic, centralized approaches refers to information storage, supposing all user and rating data distributed throughout the Semantic Web. Hence its decentralized nature. We thus come to identify several research issues: W. Lindner et al. (Eds.): EDBT 2004 Workshops, LNCS 3268, pp. 78–89, 2004. c Springer-Verlag Berlin Heidelberg 2004
Semantic Web Recommender Systems 79 Ontological Commitment. Basically, the Semantic Web is made up of machine readable content distributed all over the Web. In order to ensure that agents can understand and reason about the respective information, semantic interoperability via ontologies or common content models must be established. For instance, FOAF 5], an acronym for"Friend of a Friend", defines an ontology for establishing simple social networks and represents an open standard agents can rely upon. Interaction Facilities. Decentralized recommender systems have primarily been ubject to multi-agent research projects. In suchlike settings, environment models are agent-centric, enabling agents to directly communicate with their peers and thus making synchronous message exchange feasible. The Semantic Web, being an ag gregation of distributed metadata, constitutes an inherently data-centric environment model. Messages are exchanged by publishing or updating documents encoded in RDE OWL. or similar formats. Hence the communication becomes restricted to asynchronous message exchange only Security and Credibility. Closed communities generally possess efficient means to control the users'identity and penalize malevolent behavior. Decentralized systems nong those peer-to-peer networks, open marketplaces and the Semantic Web, like- ise, cannot prevent deception and insincerity. Spoofing and identity forging thus become facile to achieve [22]. Hence, some subjective means enabling each indi- vidual to decide which peers and content to rely upon are needed. Computational Complexity and Scalability. Centralized systems allow for es- timating and limiting the community size and may thus tailor their filtering sys- tems to ensure scalability. Note that user similarity assessment, which is an integral part of collaborative filtering [6], implies some computation-intensive processes The Semantic Web will once contain millions of machine-readable homepages. omputing similarity measures for all these "individuals"thus becomes infeasible. Consequently, scalability can only be ensured when restricting these computations to sufficiently narrow neighborhoods. Intelligent filtering mechanisms are needed, ill ensuring reasonable recall, i.e., not sacrificing too many relevant, like-minded rents. Low Profile Overlap. Interest profiles are generally represented by vectors indicat ing the user's opinion for every product. In order to reduce dimensionality and ensure profile overlap, some centralized systems like Ringo [20] require users to rate small subsets of the overall product space. These mandatory assessments, provisional tools for creating overlap-ensuring profiles, imply additional efforts for prospective users Other recommenders, among those GroupLens and MovieLens [14], operate in do- mains where product sets are comparatively small. On the Semantic Web, virtually no restrictions can be imposed on agents regarding which items to rate. Hence, new approaches to ensure profile overlap are needed in order to make profile similarity measures meaningful 3 Proposed Approach Endeavors to ensure semantical interoperability through ontologies constitute the corner- stone of Semantic Web conception and have been subject to numerous research projects
Semantic Web Recommender Systems 79 – Ontological Commitment. Basically, the Semantic Web is made up of machinereadable content distributed all over the Web. In order to ensure that agents can understand and reason about the respective information, semantic interoperability via ontologies or common content models must be established. For instance, FOAF [5], an acronym for “Friend of a Friend”, defines an ontology for establishing simple social networks and represents an open standard agents can rely upon. – Interaction Facilities. Decentralized recommender systems have primarily been subject to multi-agent research projects. In suchlike settings, environment models are agent-centric, enabling agents to directly communicate with their peers and thus making synchronous message exchange feasible. The Semantic Web, being an aggregation of distributed metadata, constitutes an inherently data-centric environment model. Messages are exchanged by publishing or updating documents encoded in RDF, OWL, or similar formats. Hence, the communication becomes restricted to asynchronous message exchange only. – Security and Credibility. Closed communities generally possess efficient means to control the users’identity and penalize malevolent behavior. Decentralized systems, among those peer-to-peer networks, open marketplaces and the Semantic Web, likewise, cannot prevent deception and insincerity. Spoofing and identity forging thus become facile to achieve [22]. Hence, some subjective means enabling each individual to decide which peers and content to rely upon are needed. – Computational Complexity and Scalability. Centralized systems allow for estimating and limiting the community size and may thus tailor their filtering systems to ensure scalability. Note that user similarity assessment, which is an integral part of collaborative filtering [6], implies some computation-intensive processes. The Semantic Web will once contain millions of machine-readable homepages. Computing similarity measures for all these “individuals” thus becomes infeasible. Consequently, scalability can only be ensured when restricting these computations to sufficiently narrow neighborhoods. Intelligent filtering mechanisms are needed, still ensuring reasonable recall, i.e., not sacrificing too many relevant, like-minded agents. – Low Profile Overlap. Interest profiles are generally represented by vectors indicating the user’s opinion for every product. In order to reduce dimensionality and ensure profile overlap, some centralized systems like Ringo [20] require users to rate small subsets of the overall product space. These mandatory assessments, provisional tools for creating overlap-ensuring profiles, imply additional efforts for prospective users. Other recommenders, among those GroupLens and MovieLens [14], operate in domains where product sets are comparatively small. On the Semantic Web, virtually no restrictions can be imposed on agents regarding which items to rate. Hence, new approaches to ensure profile overlap are needed in order to make profile similarity measures meaningful. 3 Proposed Approach Endeavors to ensure semantical interoperability through ontologies constitute the cornerstone of Semantic Web conception and have been subject to numerous research projects
80 C-N. Ziegler We do not concentrate our efforts on this aspect but suppose data compatibility from the outset. Our interest rather focuses on handling computational complexity, security, data-centric message passing, and profile vector overlap. Hereby, our approach build upon two fundamental notions, namely taxonomy-driven interest profile assembly and trust networks. The exploitation of synergies of both intrinsically separate concepts helps Is leverage recommender system facilities into the Semantic Web 3.1 Information Model The infrastructure of the Semantic Web defines interlinked XML-documents made up of machine-readable metadata. Our information model presented below well complies with its design goals and allows facile mapping into RDF, OWL, etc. Set of agents A=(a1, a2,..., an). Set A contains all agents part of the community Globally unique identifiers are assigned through URIs Set of products B=b1, b2, .. bm. All products considered are comprised in set B. Hereby, unique identifiers may refer to product descriptions from an online shop agreed upon, such as Amazon. com, or globally accepted codes, like IsBNs in case of books Set of partial trust functions T=t,, t2, .. tn. Every agent ai E A has one partial trust function t:: A+-1,+1 that assigns continuous trust values to its peers. Functions ti E A are partial since agents generally only rate small subsets of he overall community, hence rendering ti sparse: if tru ti(aj)=i, if no trust statement for a, from a (1) le define high values for ti(ai) to denote high trust from ai in aj, and negative values to express distrust, respectively. Values around zero indicate the absence of trust, not to be confused with explicit distrust [ll] Set of partial rating functions R=(r1, r2,..., Tn. In addition to functions t;∈T, every a∈ A has one partial function r;:B→[-1,+1] that expresses dislike of product bk E B. No person can rate every available product, so functions ri E B are necessarily partial P, if rates(ai, bk)=p ri(bk)=1, if no rating for b from a; Intuitively, high positive values for ri(bk ) denote that ai highly appreciates bk, while negative values express dislike, respectively Taxonomy C over set D=d1, d2,...,du. Set D contains categories for product classification. Each category de E Represents one specific topic that products bk E B may fall into. Topics express broad or narrow categories. The partial taxonomic order C: D-2 retrieves all immediate sub-categories C(de)C D for topics d∈D. Hereby, we require that C(d)nC(dn)= holds for all d,dh∈D,e≠h hence imposing tree-like structuring, similar to single-inheritance class hierarchies known from object-oriented languages. Leaf topics de are topics with zero outdegree, formally C(de)=L, i.e., most specific categories. Furthermore, taxonomy C has
80 C.-N. Ziegler We do not concentrate our efforts on this aspect but suppose data compatibility from the outset. Our interest rather focuses on handling computational complexity, security, data-centric message passing, and profile vector overlap. Hereby, our approach builds upon two fundamental notions, namely taxonomy-driven interest profile assembly and trust networks. The exploitation of synergies of both intrinsically separate concepts helps us leverage recommender system facilities into the Semantic Web. 3.1 Information Model The infrastructure of the Semantic Web defines interlinked XML-documents made up of machine-readable metadata. Our information model presented below well complies with its design goals and allows facile mapping into RDF, OWL, etc.: – Set of agents A = {a1, a2,...,an}. Set A contains all agents part of the community. Globally unique identifiers are assigned through URIs. – Set of products B = {b1, b2,...,bm}. All products considered are comprised in set B. Hereby, unique identifiers may refer to product descriptions from an online shop agreed upon, such as Amazon.com, or globally accepted codes, like ISBNs in case of books. – Set of partial trust functions T = {t1, t2,...,tn}. Every agent ai ∈ A has one partial trust function ti : A → [−1, +1]⊥ that assigns continuous trust values to its peers. Functions ti ∈ A are partial since agents generally only rate small subsets of the overall community, hence rendering ti sparse: ti(aj ) = p, if trust(ai, aj ) = p ⊥, if no trust statement for aj from ai (1) We define high values for ti(aj ) to denote high trust from ai in aj , and negative values to express distrust, respectively. Values around zero indicate the absence of trust, not to be confused with explicit distrust [11]. – Set of partial rating functions R = {r1, r2,...,rn}. In addition to functions ti ∈ T, every ai ∈ A has one partial function ri : B → [−1, +1]⊥ that expresses his liking or dislike of product bk ∈ B. No person can rate every available product, so functions ri ∈ B are necessarily partial. ri(bk) = p, if rates(ai, bk) = p ⊥, if no rating for bk from ai (2) Intuitively, high positive values for ri(bk) denote that ai highly appreciates bk, while negative values express dislike, respectively. – Taxonomy C over set D = {d1, d2,...,dl}. Set D contains categories for product classification. Each category de ∈ D represents one specific topic that products bk ∈ B may fall into. Topics express broad or narrow categories. The partial taxonomic order C : D → 2D retrieves all immediate sub-categories C(de) ⊆ D for topics de ∈ D. Hereby, we require that C(de)∩C(dh) = ∅ holds for all de, dh ∈ D, e = h, hence imposing tree-like structuring, similar to single-inheritance class hierarchies known from object-oriented languages. Leaf topics de are topics with zero outdegree, formally C(de) = ⊥, i.e., most specific categories. Furthermore, taxonomy C has
Semantic Web Recommender Systems 81 exactly one top element T, which represents the most general topic and has zero Descriptor assignment function f: B-2. Function f assigns a set Dk C D of product topics to every product bk E B. Note that products may possess everal descriptors, for classification into one single category generally entails loss of precision. We suppose all information about agents ai, their trust relationships ti and ratings ri stored in machine-readable homepages distributed throughout the Web. Contrarily, taxonomy C, set B of products and descriptor assignment function f must hold globally and therefore offer public accessibility. Central maintenance of this information hence becomes inevitable. Later on. we will demonstrate that such sources of information for product categorization already exist for certain application domains 3.2 Trust-Based Neighborhood Formation The computation of trust-based neighborhoods constitutes one pivotal pillar of our ap proach. Clearly, neighborhoods are subjective, reflecting every agent a' s very beliefs about the accorded trustworthiness of immediate peers. The incorporation of trust-based social networks basically addresses two of the above-stated issues, namely security and credibility of information provided, and computational complexity Maintaining Security and Credibility. Trust renders automatic recommendation gen eration for ai secure, making ai rely upon opinions from peers deemed trustworthy only Note that in general, collaborative filtering tends to be highly susceptive to manipula- tion. For instance, malicious agents a, can accomplish high similarity with ai simply by copying its rating profile [16] Dealing with application scenarios subject to central control and monitoring, attacks on recommender systems only pose minor concerns. Contrarily, on the Semantic Web, where, according to Tim Berners-Lee, anyone can say anything about anything", attack- resistance and robustness may become an important criterion for recommender systems Hence, as Marsh [11] already indicated, trust makes agents"less vulnerable to others Computational Complexity Reduction. However, for our scenario, trust also serves another purpose, namely that of detecting similar peers. Neighborhood formation based upon common collaborative filtering techniques requires O(A2)time complexity and thus lacks scalability for decentralized scenarios. Contrarily, trust-driven neighborhood formation schemes actually do scale well. However, substituting common neighborhood formation approaches only makes sense when ensuring that trust also reflects similarity to a certain extent: Recent studies by Swearingen and Sinha [21 provided empirical evidence that people tend to rely upon recommendations received from trusted fellows, i.e., friends, family members etc, more than upon online recommender systems. Moreover, own research endeavors [22] revealed that trust and interest similarity tend to positively correlate, justifying trust as an appropriate supplement or surrogate for collaborative filtering Results from social psychology back our findings, since positive association between
Semantic Web Recommender Systems 81 exactly one top element , which represents the most general topic and has zero indegree. – Descriptor assignment function f : B → 2D. Function f assigns a set Dk ⊆ D of product topics to every product bk ∈ B. Note that products may possess several descriptors, for classification into one single category generally entails loss of precision. We suppose all information about agents ai, their trust relationships ti and ratings ri stored in machine-readable homepages distributed throughout the Web. Contrarily, taxonomy C, set B of products and descriptor assignment function f must hold globally and therefore offer public accessibility. Central maintenance of this information hence becomes inevitable. Later on, we will demonstrate that such sources of information for product categorization already exist for certain application domains. 3.2 Trust-Based Neighborhood Formation The computation of trust-based neighborhoods constitutes one pivotal pillar of our approach. Clearly, neighborhoods are subjective, reflecting every agent a is very beliefs about the accorded trustworthiness of immediate peers. The incorporation of trust-based social networks basically addresses two of the above-stated issues, namely security and credibility of information provided, and computational complexity: Maintaining Security and Credibility. Trust renders automatic recommendation generation for ai secure, making ai rely upon opinions from peers deemed trustworthy only. Note that in general, collaborative filtering tends to be highly susceptive to manipulation. For instance, malicious agents aj can accomplish high similarity with ai simply by copying its rating profile [16]. Dealing with application scenarios subject to central control and monitoring, attacks on recommender systems only pose minor concerns. Contrarily, on the Semantic Web, where, according to Tim Berners-Lee, “anyone can say anything about anything”, attackresistance and robustness may become an important criterion for recommender systems. Hence, as Marsh [11] already indicated, trust makes agents “less vulnerable to others”. Computational Complexity Reduction. However, for our scenario, trust also serves another purpose, namely that of detecting similar peers. Neighborhood formation based upon common collaborative filtering techniques requires O(|A| 2) time complexity and thus lacks scalability for decentralized scenarios. Contrarily, trust-driven neighborhood formation schemes actually do scale well. However, substituting common neighborhood formation approaches only makes sense when ensuring that trust also reflects similarity to a certain extent: Recent studies by Swearingen and Sinha [21] provided empirical evidence that people tend to rely upon recommendations received from trusted fellows, i.e., friends, family members etc., more than upon online recommender systems. Moreover, own research endeavors [22] revealed that trust and interest similarity tend to positively correlate, justifying trust as an appropriate supplement or surrogate for collaborative filtering. Results from social psychology back our findings, since positive association between
C -N. Ziegler Fig 1. All Consuming's largest trust network cluster attitudinal similarity and interpersonal attraction has effectively become one of this discipline's most reliable findings 3] Massa[12] conducted experiments on top of the well-known Epinions rating com- munity(http://www.epinions.com),revealingthatsimilaritycomputationbasedupon common collaborative filtering approaches fails when supposing very large product sets, hence making neighborhood formation impossible. Operating on the same data, Massa found that"trust-aware techniques can produce trust scores for very high numbers of peers". Neighborhood formation thus becomes more facile to achieve. Figure 1 shows the largest cluster of the All Consuming community trust network, sualized with layouting tools we implemented for that purpose. Note that approximately 95% of all members are part of the latter cluster, isolating comparatively few peers and levelling the ground for effective neighborhood formation Trust Propagation Models. Trust-based neighborhood detection for ai, using those " trust-aware techniques"mentioned by Massa, implies deriving trust values for peers aj not directly trusted by ai, but one of the persons the latter agent trusts directly or indirectly. Note that functions ti(ai)are commonly sparse, providing values for only few aj compared to As overall community size. Hence, trust metrics exploit the"conditional transitivity"property of trust [1] and allow for rendering trust functions dense Numerous scalar metrics [4, 10] have been proposed for computing trust betwee wo given individuals a; and ai We hereby denote computed trust weights by tf(ai)as pposed to explicit trust ti (ai). However, our approach requires metrics that compute nearest trust neighbors, and not evaluate trust values for any two given agents. We hence opt for local group trust metrics[23], which have only been attracting marginal interest until now. The most important and most well-known local group trust metric is Levien's
82 C.-N. Ziegler http://adplusplus.blogspot.com/ http://www.mockerybird.com/ http://aektuesday.blogspot.com/ http://amnesia.beautiful-placebo.org/ http://beautiful-placebo.org/ http://amnesia.vacantsea.net/ http://www.paintedeyes.org/ http://anewexperiment.blogspot.com/ http://www.robot-trouble.com/kustard/ http://angelleopard83.blogspot.com/ http://oook.blogsite.org/ http://www.caterina.net/ http://ape10.blogspot.com/ http://astridiana.blogspot.com/ http://ana.typepad.com/views_of_the_northeast/ http://dean2004.blogspot.com/ http://vicsjournal.blogspot.com/ http://www.bigfatblog.com/ http://www.blogstudio.com/RebeccasRamblings/ http://www.carvegirl.com/blog/ http://www.feministe.us/blog/ http://www.happyvalleyasylum.com/ratched/ http://www.jenniferb.com/daily/ http://www.redpolka.org/ http://www.redpolka.org/blog/ http://www.veronicas.org/blog/ http://www.wehavebrains.com/ http://bakedfresh.blogspot.com/ http://www.ultratart.com/ http://blackbox.cs.fit.edu/blog/andy/ 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All Consuming’s largest trust network cluster attitudinal similarity and interpersonal attraction has effectively become one of this discipline’s most reliable findings [3]. Massa [12] conducted experiments on top of the well-known Epinions rating community (http://www.epinions.com), revealing that similarity computation based upon common collaborative filtering approaches fails when supposing very large product sets, hence making neighborhood formation impossible. Operating on the same data, Massa found that “trust-aware techniques can produce trust scores for very high numbers of peers”. Neighborhood formation thus becomes more facile to achieve. Figure 1 shows the largest cluster of theAll Consuming community trust network, visualized with layouting tools we implemented for that purpose. Note that approximately 95% of all members are part of the latter cluster, isolating comparatively few peers and levelling the ground for effective neighborhood formation. Trust Propagation Models. Trust-based neighborhood detection for ai, using those “trust-aware techniques” mentioned by Massa, implies deriving trust values for peers aj not directly trusted by ai, but one of the persons the latter agent trusts directly or indirectly. Note that functionsti(aj ) are commonly sparse, providing values for only few aj compared to A’s overall community size. Hence, trust metrics exploit the “conditional transitivity” property of trust [1] and allow for rendering trust functions dense. Numerous scalar metrics [4, 10] have been proposed for computing trust between two given individuals ai and aj . We hereby denote computed trust weights by t c i (aj ) as opposed to explicit trust ti(aj ). However, our approach requires metrics that compute nearest trust neighbors, and not evaluate trust values for any two given agents. We hence opt for local group trust metrics [23], which have only been attracting marginal interest until now. The most important and most well-known local group trust metric is Levien’s
Semantic Web Recommender Systems 83 Advogato metric [10]. However, the metric can only make boolean decisions with respect to trustworthiness, classifying agents into trusted and untrusted ones. Appleseed [23], our own novel proposal for local group trust computation, allows more fine-grained analysis, assigning continuous trust weights for peers within trust computation range. Rankings thus become feasible. Appleseed's principal concepts de rive from spreading activation models [18], which have been conceived for modelling human semantic memory. Appleseed operates on partial trust graph information, explor ing the social network within predefined ranges only and allowing the neighborhood detection process ro retain scalability. Hereby, high ranks are accorded to trustworthy peers, i.e., those agents which are largely trusted by others with high trustworthiness, similar to PageRank [17]. These ranks are used later on for selecting agents deemed suitable for making recommendations 3.3 Taxonomy.Driven Similarity Metrics Trust allows selecting peers with overall above-average interest similarity. However, for each active user, some peers having completely opposed interests generally exist. The proposition that interpersonal attraction, and hence trust, implies attitudinal similarit does not always hold true. Supplementary filtering thus becomes indispensable. Two approaches are conceivable User-User Closeness. Additional filtering based upon profile similarity c(ai, ai) between agents ai, ai is applied to the neighborhood of ais trustworthy peers. The resulting set of peers only contains trusted peers which are guaranteed to resemble the active user ai Product-USer Relevance. Instead of pruning the very neighborhood, one could also consider the set of all products bk appreciated by at least one of ais neighbors and dispose of those products not fitting the interest profile of ai. We hereby denote the relevance of bk for ai by c(ai, bk) Or For both cases, the filtering task faces the problem of low profile overlap by virtue 3 information sparseness and potentially large product sets [12]. In order to alleviate prevailing issue, we propose taxonomy-driven profile computation [22, 24], which allows to derive similarity between users a; and a, even though these peers have not rated one product in common. Moreover, our novel filtering method also permits to compute similarity between two products bk, bh Profile Generation. Our approach to taxonomy-driven generation of interest profiles [22, 24] extends basic ideas derived from Middleton's ontology-enhanced content-based filtering method [13]. In contrast to generic feature-based filtering, product categories still play an important role, but we have them arranged in a taxonomy and not separate from each other. Products bk bear topic descriptors dke e f(bk) that relate these bk to taxonomic nodes. Several classifications per product are possible, hence f(bk)>1 Each product the user likes infers some interest score for those dke E f(bk). Since these categories dke are arranged in taxonomy C, we can also infer a fractional interest for all super-topics of dke. Hereby, remote super-topics are accorded less interest score than super-topics close to dke. Assume that(p0, P1, .. Pa) gives the path from top element
Semantic Web Recommender Systems 83 Advogato metric [10]. However, the metric can only make boolean decisions with respect to trustworthiness, classifying agents into trusted and untrusted ones. Appleseed [23], our own novel proposal for local group trust computation, allows more fine-grained analysis, assigning continuous trust weights for peers within trust computation range. Rankings thus become feasible. Appleseed’s principal concepts derive from spreading activation models [18], which have been conceived for modelling human semantic memory. Appleseed operates on partial trust graph information, exploring the social network within predefined ranges only and allowing the neighborhood detection process ro retain scalability. Hereby, high ranks are accorded to trustworthy peers, i.e., those agents which are largely trusted by others with high trustworthiness, similar to PageRank [17]. These ranks are used later on for selecting agents deemed suitable for making recommendations. 3.3 Taxonomy-Driven Similarity Metrics Trust allows selecting peers with overall above-average interest similarity. However, for each active user, some peers having completely opposed interests generally exist. The proposition that interpersonal attraction, and hence trust, implies attitudinal similarity does not always hold true. Supplementary filtering thus becomes indispensable. Two approaches are conceivable: – User-User Closeness. Additional filtering based upon profile similarity c(ai, aj ) between agents ai, aj is applied to the neighborhood of ai’s trustworthy peers. The resulting set of peers only contains trusted peers which are guaranteed to resemble the active user ai. – Product-User Relevance. Instead of pruning the very neighborhood, one could also consider the set of all products bk appreciated by at least one of ai’s neighbors and dispose of those products not fitting the interest profile of ai. We hereby denote the relevance of bk for ai by cb(ai, bk). For both cases, the filtering task faces the problem of low profile overlap by virtue of information sparseness and potentially large product sets [12]. In order to alleviate the prevailing issue, we propose taxonomy-driven profile computation [22, 24], which allows to derive similarity between users ai and aj even though these peers have not rated one product in common. Moreover, our novel filtering method also permits to compute similarity between two products bk, bh. Profile Generation. Our approach to taxonomy-driven generation of interest profiles [22, 24] extends basic ideas derived from Middleton’s ontology-enhanced content-based filtering method [13]. In contrast to generic feature-based filtering, product categories still play an important role, but we have them arranged in a taxonomy and not separate from each other. Products bk bear topic descriptors dke ∈ f(bk) that relate these bk to taxonomic nodes. Several classifications per product are possible, hence |f(bk)| ≥ 1. Each product the user likes infers some interest score for those dke ∈ f(bk). Since these categories dke are arranged in taxonomy C, we can also infer a fractional interest for all super-topics of dke . Hereby, remote super-topics are accorded less interest score than super-topics close to dke . Assume that (p0, p1,...,pq) gives the path from top element
C.N. Ziegler Cetane sps Fig. 2. Small fragment from the Amazon. com book taxonomy Po=T to node pa =dke. Function sib(p)returns the number of p's siblings, while sco(p) returns p's score: m∈{0,1,,q-1}:sco(m)=F Scores are normalized, i. e, all topic score that ais profile assigns to nodes from axonomy C amounts to some fixed value s. Hence, high product ratings from agents with short product rating histories have higher impact on profile generation than product ratings from persons issuing rife ratings. Score s is divided evenly among all products that contribute to ai's profile makeup. Factor K permits fine-tuning the extent of super-topic score inference, depending on the underlying taxonomy's depth and granularity Example 1(Topic Score Assignment). Suppose the taxonomy given in Figure 2 which represents a tiny fragment from the Amazon. com book taxonomy, and propagation factor K=l Let user a; have mentioned 4 books, namely Matrix Analysis, Fermat's Enigma Snow Crash, and Neuromancer For Matrix Analysis, 5 topic descriptors are given,one of them pointing to leaf topic ALGEBRA within our small taxonomy. are Pure, Mathematics, Science, and top element Books. Score 50 hence must be divided among these topics according to Equation 3. Score 29. 087 becomes accorded to topic Algebra. Likewise, we get 14.543 for topic Pure, 4.848 for Mathematics, 1.212 for Science, and 0.303 for top element Books. These values are then used to build the profile vector of user ai Success or failure of our approach largely depends upon taxonomy C used for clas- sification. The more thoroughly crafted and fine-grained the latter taxonomy, the more meaningful our profile information becomes Clearly, topic descriptors f(bk) for prod ucts bk must be chosen skillfully, too. By virtue of inference of fractional interest for super-topics, one may establish high user similarity for users which have not even rated one single product in common, as has been indicated before. According to our scheme, the more score two profiles have accumulated in same branches, the higher their com- puted similarity
84 C.-N. Ziegler Books Science Mathematics Reference Sports Archaelogy Medicine Applied Pure Nonfiction Discrete Algebra History Astronomy Fig. 2. Small fragment from the Amazon.com book taxonomy p0 = to node pq = dke . Function sib(p) returns the number of p’s siblings, while sco(p) returns p’s score: ∀m ∈ {0, 1,...,q − 1} : sco(pm) = κ · sco(pm+1) sib(pm+1)+1 (3) Scores are normalized, i.e., all topic score that ai’s profile assigns to nodes from taxonomy C amounts to some fixed value s. Hence, high product ratings from agents with short product rating histories have higher impact on profile generation than product ratings from persons issuing rife ratings. Score s is divided evenly among all products that contribute to ai’s profile makeup. Factor κ permits fine-tuning the extent of super-topic score inference, depending on the underlying taxonomy’s depth and granularity. Example 1 (Topic Score Assignment). Suppose the taxonomy given in Figure 2 which represents a tiny fragment from theAmazon.com book taxonomy, and propagation factor κ = 1. Let user ai have mentioned 4 books, namely Matrix Analysis, Fermat’s Enigma, Snow Crash, and Neuromancer. For Matrix Analysis, 5 topic descriptors are given, one of them pointing to leaf topic Algebra within our small taxonomy. Suppose that s = 1000 defines the overall accorded profile score. Then the score assigned to descriptor Algebra amounts to s / (4 · 5) = 50. Ancestors of leaf Algebra are Pure, Mathematics, Science, and top element Books. Score 50 hence must be divided among these topics according to Equation 3. Score 29.087 becomes accorded to topic Algebra. Likewise, we get 14.543 for topic Pure, 4.848 for Mathematics, 1.212 for Science, and 0.303 for top element Books. These values are then used to build the profile vector of user ai. Success or failure of our approach largely depends upon taxonomy C used for classification. The more thoroughly crafted and fine-grained the latter taxonomy, the more meaningful our profile information becomes. Clearly, topic descriptors f(bk) for products bk must be chosen skillfully, too. By virtue of inference of fractional interest for super-topics, one may establish high user similarity for users which have not even rated one single product in common, as has been indicated before. According to our scheme, the more score two profiles have accumulated in same branches, the higher their computed similarity
Semantic Web Recommender Systems Similarity Computation. Taxonomy-driven interest profiles form the grounding for our novel filtering paradigm Similarity computation between agents ai, ai, and between agents ai and products bk, respectively, requires some distance metric For our approach, we apply common nearest-neighbor techniques, namely Pearson correlation[6, 20] and cosine distance known from information retrieval. Hereby, profile vectors map category score vectors from C instead of plain product-rating vectors. High similarity evolves from interest in many identical or related branches, whereas negative orrelation indicates diverging interests. For instance, suppose ai reads literature about Applied Mathematics only, and about Algebra, then their computed similarity will be high, considering si ant branch overlap from node Mathematics onward 3. 4 Recommendation Generation We already indicated that two alternative designs are viable for post-filtering trust neighborhoods. We opt for product-user relevance, i. e, deferring supplementary neighborhood filtering into the recommendation process. We hence consider all peers art of the trust neighborhood, but weed out products not matching the active user on various factors, the two most important aspects being the following onef. n depends The relevance of some product bk one of ais trusted peers recommends the Accorded trust t(ai )of peers a, mentioning bk. Trust-based neighborhood for mation substitutes finding nearest neighbors based upon interest similarity. Likewise, similarity ranks c(ai, ai) become replaced by trust weights ti(ai) for computing the predicted relevance of bk for ai Content-based relevance cb(ai, bk )of product bk for user ai. Besides mere trust- worthiness of peers a, rating product bk, the content-based relevance of bk for the active user ai is likewise important, e.g., one may consider the situation where even close friends recommend products not fitting our interest profile at all Since both functions, i.e., tf(a,) and c(ai, bk), operate on completely different scales, the conversion of these absolute weights into ranks seems appropriate. For in- stance,the trusted agent ai with highest trust weight t(ai)obtains rank 1, and so forth Likewise, the product bk with highest similarity cb(ai, bk)to the active user ai's interest profile becomes top-ranked. Besides merging trustworthiness and content-based relevance ranks, other factors involved comprise product rating frequency and product description richness. Product rating frequency intends to reward products bk recommended by numerous trusted peers, while description richness penalizes products bearing overly general taxonomic descrip- tions. The final ranks of products bk eventually constitute the foundation for assemblin top-N recommendation lists for the active user ai Supposing implicit product ratings only, the generation of taxonomy-driven profiles for products bk equates profile generation for pseudo-user ae having implicitly rated bp
Semantic Web Recommender Systems 85 Similarity Computation. Taxonomy-driven interest profiles form the grounding for our novel filtering paradigm. Similarity computation between agents ai, aj , and between agents ai and products bk 1, respectively, requires some distance metric. For our approach, we apply common nearest-neighbor techniques, namely Pearson correlation [6, 20] and cosine distance known from information retrieval. Hereby, profile vectors map category score vectors from C instead of plain product-rating vectors. High similarity evolves from interest in many identical or related branches, whereas negative correlation indicates diverging interests. For instance, suppose ai reads literature about Applied Mathematics only, and aj about Algebra, then their computed similarity will be high, considering significant branch overlap from node Mathematics onward. 3.4 Recommendation Generation We already indicated that two alternative designs are viable for post-filtering trust neighborhoods. We opt for product-user relevance, i.e., deferring supplementary neighborhood filtering into the recommendation process. We hence consider all peers part of the trust neighborhood, but weed out products not matching the active user ai’s profile. The relevance of some product bk one of ai’s trusted peers recommends then depends on various factors, the two most important aspects being the following ones: – Accorded trust t c i (aj ) of peers aj mentioning bk. Trust-based neighborhood formation substitutes finding nearest neighbors based upon interest similarity. Likewise, similarity ranks c(ai, aj ) become replaced by trust weights t c i (aj ) for computing the predicted relevance of bk for ai. – Content-based relevance cb(ai, bk) of product bk for user ai. Besides mere trustworthiness of peers aj rating product bk, the content-based relevance of bk for the active user ai is likewise important, e.g., one may consider the situation where even close friends recommend products not fitting our interest profile at all. Since both functions, i.e., t c i (aj ) and cb(ai, bk), operate on completely different scales, the conversion of these absolute weights into ranks seems appropriate. For instance, the trusted agent aj with highest trust weight t c i (aj ) obtains rank 1, and so forth. Likewise, the product bk with highest similarity cb(ai, bk) to the active user ai’s interest profile becomes top-ranked. Besides merging trustworthiness and content-based relevance ranks, other factors involved comprise product rating frequency and product description richness. Product rating frequency intends to reward products bk recommended by numerous trusted peers, while description richness penalizes products bearing overly general taxonomic descriptions. The final ranks of products bk eventually constitute the foundation for assembling top-N recommendation lists for the active user ai. 1 Supposing implicit product ratings only, the generation of taxonomy-driven profiles for products bk equates profile generation for pseudo-user aθ having implicitly rated bk only
86 C-N. Ziegler 3.5 Supplementary Fine-Tunir Taxonomy-driven profile generation renders another mechanism, dubbed "topic diver sification"[24], feasible. Hereby, our novel approach, optionally applicable on top of recommendation generation, allows rearrangement of the active user ais recommen- dation list in order to better reflect ais full range of interests, considering the impact of specific topics ai implicitly declares interest in. For instance, suppose that novels classifying under Modern German Poetry have twice the share of Social Psychology in a'is reading list. Then post-processing of a;'s recommendation list by means of topic diversification procedures allows to fully account for that fact To our best knowledge, no similar approaches exist or have been documented in literature affiliated with recommender systems. Moreover, topic diversification becomes even more valuable when making recommendations across diverse domains of interest e.g, books, DVDs, apparel, etc. Other enhancements include considering explicit product ratings for recommenda- tion generation whenever available. However, note that most scenarios only allow for collecting implicit ratings, e.g., purchase data, product mentions, etc, rather than explicit 4 Real-World Deployment Section 3. 1 exposed our envisioned information infrastructure. We will show that such an architecture may actually come into life and become an integral part of the Semantic Web Social Networks. FOAF defines machine-readable homepages based upon RDF and allows weaving acquaintance networks. Golbeck [5] proposed some modifica tions making FOAF support"real"trust relationships instead of mere acquaintance ship. Product Rating Information. Moreover, FOAF networks seamlessly integrate with so-called"weblogs", which are steadily gaining momentum. These personalized online diaries"are especially valuable with respect to product rating information For instance, some crawlers extract certain hyperlinks from weblogs and analyze their makeup and content. Hereby, those referring to product pages from large cat alogslikeAmazon.com(http://www.amazon.com)countasimplicitvotesforthese goods. Mappings between hyperlinks and some sort of unique identifier are required for diverse catalogs, though. Unique identifiers exist for some product groups like books, which are given"International Standard Book Numbers, i.e., ISBNS. Ef forts to enhance weblogs with explicit, machine-readable rating information have also been proposed and are becoming increasingly popular. For instance, BLAM! (http://www.pmbrowserinfo/hublog/)allowscreatingbookratingsandhelpsem- bedding these into machine-readable weblogs Product Classification Taxonomies. besides user-centric information, i.e. agent ais trust relationships ti and product ratings ri, taxonomies for product classifica- tion play an important role within our approach. Luckily, these taxonomies exist for
86 C.-N. Ziegler 3.5 Supplementary Fine-Tuning Taxonomy-driven profile generation renders another mechanism, dubbed “topic diversification” [24], feasible. Hereby, our novel approach, optionally applicable on top of recommendation generation, allows rearrangement of the active user ai’s recommendation list in order to better reflect ai’s full range of interests, considering the impact of specific topics ai implicitly declares interest in. For instance, suppose that novels classifying under Modern German Poetry have twice the share of Social Psychology in a i’s reading list. Then post-processing of ai’s recommendation list by means of topic diversification procedures allows to fully account for that fact. To our best knowledge, no similar approaches exist or have been documented in literature affiliated with recommender systems. Moreover, topic diversification becomes even more valuable when making recommendations across diverse domains of interest, e.g., books, DVDs, apparel, etc. Other enhancements include considering explicit product ratings for recommendation generation whenever available. However, note that most scenarios only allow for collecting implicit ratings, e.g., purchase data, product mentions, etc., rather than explicit ones. 4 Real-World Deployment Section 3.1 exposed our envisioned information infrastructure. We will show that such an architecture may actually come into life and become an integral part of the Semantic Web: – Social Networks. FOAF defines machine-readable homepages based upon RDF and allows weaving acquaintance networks. Golbeck [5] proposed some modifications making FOAF support “real” trust relationships instead of mere acquaintanceship. – Product Rating Information. Moreover, FOAF networks seamlessly integrate with so-called “weblogs”, which are steadily gaining momentum. These personalized “online diaries” are especially valuable with respect to product rating information. For instance, some crawlers extract certain hyperlinks from weblogs and analyze their makeup and content. Hereby, those referring to product pages from large catalogs like Amazon.com (http://www.amazon.com) count as implicit votes for these goods. Mappings between hyperlinks and some sort of unique identifier are required for diverse catalogs, though. Unique identifiers exist for some product groups like books, which are given “International Standard Book Numbers”, i.e., ISBNs. Efforts to enhance weblogs with explicit, machine-readable rating information have also been proposed and are becoming increasingly popular. For instance, BLAM! (http://www.pmbrowser.info/hublog/) allows creating book ratings and helps embedding these into machine-readable weblogs. – Product Classification Taxonomies. Besides user-centric information, i.e., agent ai’s trust relationships ti and product ratings ri, taxonomies for product classification play an important role within our approach. Luckily, these taxonomies exist for
Semantic Web Recommender Systems 8 certain domains. Amazon. com defines an extensive, fine-grained and deeply-nested taxonomyforbookscontainingthousandsoftopicsMoreimportant,amazon.com provides books with subject descriptors referring to the latter taxonomy. Similar taxonomies exist for DVDs, CDs, and videos. Standardization efforts for product classification are channelled through the "United Nations Standard Products and ServicesCodeproject(http://www.unspsc.org/).However,theUnspSc'staxon omy provides much less information and nesting than, for instance, Amazon. coms taxonomy for books. 5 Experimental Setting We created an experimental environment simulating the infrastructure proposed above hereby, by means of crawlers and screen scrapers, we gathered information from vari oustrust-awareonlinecommunitieslikeAllConsuming(http://www.allconsuming.net), andAdvogato(http://www.advogato.org),extractinginformationaboutapproximately 10, 000 users ai, their trust relationships ti and implicit product ratings ri. Ratings were obtained from All Consuming only. moreover, we captured Amazon coms huge book taxonomy, made up of 13, 525 hierarchically arranged topics de, and categorization data about 11, 031 books bk that All Consuming community members mentioned Tailored crawlers search the Web for weblogs and ensure data freshness. All our experiments and empirical evaluations were based upon this"real-world"data. 6 Related work Recommender systems have begun attracting major research interest during the early nineties [6]. Nowadays, commercial and industrial systems are rife and wide-spread, detailed comparisons concerning features and approaches are given in [19]. Recom- mender systems differ from each other mainly through their filtering method. Hereby, distinctions between three types of filtering systems are made [6], namely collaborative, content-based and economic Collaborative filtering systems [20] generate recommenda tions obtained from persons having similar interests. Content-based filtering only takes into account descriptions of products, based upon metadata and extracted features. Eco- nomic filtering has seen little practical application until now and exerts marginal impact only Modern recommender systems are hybrid, combining both content-based and col- laborative filtering facilities in one single framework. Fab [2] counts among the first popular hybrid systems, more recent approaches have been depicted in [7] and [13] Our filtering approach, comprising taxonomy-based profile generation and similarity computation, also exploits both content-based and collaborative filtering facilities. Trust networks add another supplementary level of filtering Initial attempts have been taken towards transplanting recommender systems into decentralized scenarios. Olsson [15]gives an extensive overview of existing approaches Kautz et al. [9] extract implicit social network structures from the Web, using them
Semantic Web Recommender Systems 87 certain domains. Amazon.com defines an extensive, fine-grained and deeply-nested taxonomy for books, containing thousands of topics. More important, Amazon.com provides books with subject descriptors referring to the latter taxonomy. Similar taxonomies exist for DVDs, CDs, and videos. Standardization efforts for product classification are channelled through the “United Nations Standard Products and Services Code” project (http://www.unspsc.org/). However, the UNSPSC’s taxonomy provides much less information and nesting than, for instance, Amazon.com’s taxonomy for books. 5 Experimental Setting We created an experimental environment simulating the infrastructure proposed above. Hereby, by means of crawlers and screen scrapers, we gathered information from various trust-aware online communities like All Consuming (http://www.allconsuming.net), and Advogato (http://www.advogato.org), extracting information about approximately 10, 000 users ai, their trust relationships ti and implicit product ratings ri. Ratings were obtained from All Consuming only. Moreover, we captured Amazon.com’s huge book taxonomy, made up of 13, 525 hierarchically arranged topics de, and categorization data about 11, 031 books bk that All Consuming community members mentioned. Tailored crawlers search the Web for weblogs and ensure data freshness. All our experiments and empirical evaluations were based upon this “real-world” data. 6 Related Work Recommender systems have begun attracting major research interest during the early nineties [6]. Nowadays, commercial and industrial systems are rife and wide-spread, detailed comparisons concerning features and approaches are given in [19]. Recommender systems differ from each other mainly through their filtering method. Hereby, distinctions between three types of filtering systems are made [6], namely collaborative, content-based and economic. Collaborative filtering systems [20] generate recommendations obtained from persons having similar interests. Content-based filtering only takes into account descriptions of products, based upon metadata and extracted features. Economic filtering has seen little practical application until now and exerts marginal impact only. Modern recommender systems are hybrid, combining both content-based and collaborative filtering facilities in one single framework. Fab [2] counts among the first popular hybrid systems, more recent approaches have been depicted in [7] and [13]. Our filtering approach, comprising taxonomy-based profile generation and similarity computation, also exploits both content-based and collaborative filtering facilities. Trust networks add another supplementary level of filtering. Initial attempts have been taken towards transplanting recommender systems into decentralized scenarios. Olsson [15] gives an extensive overview of existing approaches. Kautz et al. [9] extract implicit social network structures from the Web, using them