PaperlD: 213 Scienstein: A Research Paper recommender System Bela Gipp, Joran Beel, Christian Hentschel I Otto-von-Guericke University, Dept of Computer Science, Magdeburg, Germany Fraunhofer Institute for TelecommunicationS, Berlin, German Bela@Gipp.com,Joeran@Beel.org,christianhentschel@hhi,fraunhofer.de Abstract This paper introduces Scienstein, the first hybrid research introduces Scienstein and discusses the technologies used. the aper recommender system and a powerful alternative to focus lies on a hybrid recommender approach, which currently used academic search engines. Scienstein improves combines content-based and collaborative-based approaches the approach of the usually used keyword-based search by It shows that many of the disadvantages of existing systems combining it with citation analysis, author analysis, source become obsolete by combining known concepts with new analysis, implicit ratings, explicit ratings and in addition, ones. The last part of the paper gives insights into the usage of innovative and yet unused methods like the Distance the software by illustrating its functionality with screenshots Similarity Index'DSi) and the 'In-text Impact Factor(tlF Instead of entering just keywords, a user may provide entire documents, including reference lists as input and make IL RELATED WORK implicit and explicit ratings to improve recommendations With citation, author and source analysis, similar and related In practice, research paper recommender systems do not documents are easily determinable. All these techniques are However, concepts have been published and managed by a user-friendly gul implemented that could be used for their realisation. authors suggest using collaborative filtering and ratings Index Terms--DSI. Recommendation. Recommender Systems. Ratings could be directly obtained by considering citations as Research paper ratings [2 or implicitly generated by monitoring readers actions such as bookmarking or downloading a paper 31,[4] Citation databases such as CiteSeer apply citation analysis . INTRODUCTION (e.g. bibliographic coupling [5] or co-citation analysis [6] Many scientists consider the search for related work as an [7), in order to identify papers that are similar to an input extremely time-consuming part of their responsibilities. The paper [8]. Scholarly search engines such as Google Schol enormity of time taken is partly caused by the increasing focus on classic text mining and citation counts number of publications, which grows exponentially at a yearly Each concept does have disadvantages, which limits its rate of 3. 7 %[1]. The strength of currently used academic suitability for generating recommendations search engines lies in finding documents containing specific For example, citation analysis cannot identify homographs keywords. Due to synonyms and unclear nomenclatures, this and not all research papers are listed in citation databases Likewise. reference lists can contain irrelevant entries caused In this paper we present Scienstein', a hybrid recommender by the Matthew Effect, self citations, citation circles and system, which uses both content-based and collaborative- ceremonial citations based techniques. We believe that thi Other problems pop up with text-based analysis, which has potential to alleviate the problem of finding relevant research to cope with unclear nomenclatures, synonyms or context papers. Instead of solely relying on text mining, Scienstein depending on the meanings of words. Accordingly, text-based combines citation analysis, implicit ratings, explicit ratings author analysis and source analysis to a recommender system homographs describe authors with identical names. As a result, citation with a user-friendly GUL. Currently, Scienstein is in the analysis sometimes cannot assign a research paper to its correct author[91 development stage and open for cooperation. The Matthew Effect describes the fact that frequently cited publ m The first part of this paper gives an overview of related are more likely to be cited just because the author believes that well-known work including a discussion of the advantages and papers should be included o Sometimes self citations are made to promote other publications of the disadvantages of existing approaches. The main part author, although they are irrelevant for the citing publication [11] s Citation circles occur if citations were made to promote the work of others, although they are pointless [12] Ceremonial citations are citations that were used although the author did not read the cited publication 9
PaperID: 213 1 Abstract This paper introduces Scienstein, the first hybrid research paper recommender system and a powerful alternative to currently used academic search engines. Scienstein improves the approach of the usually used keyword-based search by combining it with citation analysis, author analysis, source analysis, implicit ratings, explicit ratings and in addition, innovative and yet unused methods like the ‘Distance Similarity Index’ (DSI) and the ‘In-text Impact Factor’ (ItIF). Instead of entering just keywords, a user may provide entire documents, including reference lists as input and make implicit and explicit ratings to improve recommendations. With citation, author and source analysis, similar and related documents are easily determinable. All these techniques are managed by a user-friendly GUI. Index Terms—DSI, Recommendation, Recommender Systems, Research paper I. INTRODUCTION Many scientists consider the search for related work as an extremely time-consuming part of their responsibilities. The enormity of time taken is partly caused by the increasing number of publications, which grows exponentially at a yearly rate of 3.7 % [1]. The strength of currently used academic search engines lies in finding documents containing specific keywords. Due to synonyms and unclear nomenclatures, this approach delivers in practice, often unsatisfying results. In this paper we present Scienstein1 , a hybrid recommender system, which uses both content-based and collaborativebased techniques. We believe that this approach has the potential to alleviate the problem of finding relevant research papers. Instead of solely relying on text mining, Scienstein combines citation analysis, implicit ratings, explicit ratings, author analysis and source analysis to a recommender system with a user-friendly GUI. Currently, Scienstein is in the development stage and open for cooperation. The first part of this paper gives an overview of related work including a discussion of the advantages and disadvantages of existing approaches. The main part 1 www.scienstein.org introduces Scienstein and discusses the technologies used. The focus lies on a hybrid recommender approach, which combines content-based and collaborative-based approaches. It shows that many of the disadvantages of existing systems become obsolete by combining known concepts with new ones. The last part of the paper gives insights into the usage of the software by illustrating its functionality with screenshots. II. RELATED WORK In practice, research paper recommender systems do not exist. However, concepts have been published and partly implemented that could be used for their realisation. Some authors suggest using collaborative filtering and ratings. Ratings could be directly obtained by considering citations as ratings [2] or implicitly generated by monitoring readers’ actions such as bookmarking or downloading a paper [3], [4]. Citation databases such as CiteSeer apply citation analysis (e.g. bibliographic coupling [5] or co-citation analysis [6], [7]), in order to identify papers that are similar to an input paper [8]. Scholarly search engines such as Google Scholar focus on classic text mining and citation counts. Each concept does have disadvantages, which limits its suitability for generating recommendations. For example, citation analysis cannot identify homographs2 , and not all research papers are listed in citation databases. Likewise, reference lists can contain irrelevant entries caused by the Matthew Effect3 , self citations4 , citation circles5 and ceremonial citations6 . Other problems pop up with text-based analysis, which has to cope with unclear nomenclatures, synonyms or context depending on the meanings of words. Accordingly, text-based 2 Homographs describe authors with identical names. As a result, citation analysis sometimes cannot assign a research paper to its correct author [9]. 3 The Matthew Effect describes the fact that frequently cited publications are more likely to be cited just because the author believes that well-known papers should be included [10]. 4 Sometimes self citations are made to promote other publications of the author, although they are irrelevant for the citing publication [11]. 5 Citation circles occur if citations were made to promote the work of others, although they are pointless [12]. 6 Ceremonial citations are citations that were used although the author did not read the cited publication [9]. Scienstein: A Research Paper Recommender System Bela Gipp1 , Jöran Beel1 , Christian Hentschel2 1 Otto-von-Guericke University, Dept. of Computer Science, Magdeburg, Germany 2 Fraunhofer Institute for Telecommunications, Berlin, Germany Bela@Gipp.com, Joeran@Beel.org, christian.hentschel@hhi.fraunhofer.de
PaperlD: 213 recommender systems cannot identify related papers if V. SCIENSTEIN'S CITATION ANALYSIS different terms are used Collaborative filtering in the domain of research paper Scienstein combines four approaches of citation analysis to recommendation is criticised for various reasons. Some identify papers that are similar to a given input paper(see authors claim that collaborative filtering would be ineffective Figure 2 for illustration). The cited by' approach considers in domains where more items than users exist [13]. Others papers relevant that cite the input document(see Figure 2 believe that users would be unwilling to spend time for documents A and B). Thereference list'approach considers explicitly rating research papers [2]. Problematic with implicit papers relevant that were referenced in the input document ratings is that for obtaining the required data, continuous (see Figure 2, documents C and D). ' Bibliographic coupling monitoring of the researcher's work is necessary, which raises considers papers relevant that cite the same article(s) as the privacy issues. In general, collaborative filtering has to cope input document(see Figure 2, document BibCo).With'co- with the possibility of manipulation. Another drawback is that citation analysis, papers are considered relevant that were a critical mass of ratings and users is required to receive useful cited by those papers that were also cited by the input document(see Figure 2, document CoCit II. SCIENSTEIN: A HYBRID RECOMMENDER SYSTEM DOC A Output Text Naive bayes and support vector For instance bibi CoIt ReferencesHcoupling,co-citation,cited by For instance reputation and and source Sources h further paper of that source DOC D I Ratings H Collaborative Filtering Figure 1: Scienstein's approach to recommend research papers Figure 2: Co-citation, bibliographic coupling, cited by and reference list Since all current search engines and concepts for researcn To rank results, Scienstein applies what we call " in-text paper recommender systems focus mainly on one approach (text analysis, citation analysis citation frequency analysis'(ICFA)and in-text citation suffers the disadvantages mentioned atings ) each concept distance analysis(ICDA) above. The Scienstein project aims to combine the already known concepts with new ICFA analyses the frequency with which a research paper is ones in order to create a holistic research paper recommender ited within th system. By combining different concepts, many disadvantages Impact Factor(ItIF), which represents the number of citations become obsolete. Scienstein's approach to recommend referring to a certain document divided by the overall number research papers is illustrated in Figure 1. With Scienstein of citations(see Figure 3 for illustration). The sum of all ItIF users may provide one or several of the six inputs(text, of one document always adds up to 1. The higher the ItIF,the references, authors, sources, ratings or documents), adjust the algorithms to their needs and receive recommendations for closer related are the input document to the cited documen research papers. Further plans for the future include broadening Scienstein's functionality so that authors, journals or conferences can also be recommended In addition to the technical side. Scienstein offers a user friendly GUI so that the complex technical possibilities can be handled without expert knowledge in formulating search queries etc If document usage is pe he usage data could. for instance. draw conclusions about the researchers yorking times and productivity For instance, put more weight on finding papers similar to the input document or finding papers published by the same or a similar authe
PaperID: 213 2 recommender systems cannot identify related papers if different terms are used. Collaborative filtering in the domain of research paper recommendation is criticised for various reasons. Some authors claim that collaborative filtering would be ineffective in domains where more items than users exist [13]. Others believe that users would be unwilling to spend time for explicitly rating research papers [2]. Problematic with implicit ratings is that for obtaining the required data, continuous monitoring of the researcher’s work is necessary, which raises privacy issues7 . In general, collaborative filtering has to cope with the possibility of manipulation. Another drawback is that a critical mass of ratings and users is required to receive useful recommendations. III. SCIENSTEIN: A HYBRID RECOMMENDER SYSTEM Input Text Algorithms Output For instance nearest neighbor, naive bayes and support vector machines For instance bibliographic coupling, co-citation, cited by and reference list Recommen- dations for papers, authors F and sources or instance reputation and further papers References Authors Sources Ratings Collaborative Filtering Further paper of that source Figure 1: Scienstein's approach to recommend research papers Since all current search engines and concepts for research paper recommender systems focus mainly on one approach (text analysis, citation analysis or ratings), each concept suffers the disadvantages mentioned above. The Scienstein project aims to combine the already known concepts with new ones in order to create a holistic research paper recommender system. By combining different concepts, many disadvantages become obsolete. Scienstein's approach to recommend research papers is illustrated in Figure 1. With Scienstein, users may provide one or several of the six inputs (text, references, authors, sources, ratings or documents), adjust the algorithms to their needs8 , and receive recommendations for research papers. Further plans for the future include broadening Scienstein’s functionality so that authors, journals or conferences can also be recommended. In addition to the technical side, Scienstein offers a userfriendly GUI so that the complex technical possibilities can be handled without expert knowledge in formulating search queries etc. 7 If document usage is permanently monitored, employers with access to the usage data could, for instance, draw conclusions about the researchers’ working times and productivity. 8 For instance, put more weight on finding papers similar to the input document or finding papers published by the same or a similar author. IV. SCIENSTEIN’S CITATION ANALYSIS Scienstein combines four approaches of citation analysis to identify papers that are similar to a given input paper (see Figure 2 for illustration). The ‘cited by’ approach considers papers relevant that cite the input document (see Figure 2, documents A and B). The ‘reference list’ approach considers papers relevant that were referenced in the input document (see Figure 2, documents C and D). 'Bibliographic coupling' considers papers relevant that cite the same article(s) as the input document (see Figure 2, document BibCo). With 'cocitation analysis', papers are considered relevant that were cited by those papers that were also cited by the input document (see Figure 2, document CoCit). Doc A Doc B Input Doc Doc BibCo Doc CoCit Doc C Doc D cites cites cites cites cites cites Figure 2: Co-citation, bibliographic coupling, cited by and reference list To rank results, Scienstein applies what we call ‘in-text citation frequency analysis’ (ICFA) and ‘in-text citation distance analysis’ (ICDA). ICFA analyses the frequency with which a research paper is cited within the citing document. We developed the ‘In-text Impact Factor’ (ItIF), which represents the number of citations referring to a certain document divided by the overall number of citations (see Figure 3 for illustration). The sum of all ItIF of one document always adds up to 1. The higher the ItIF, the closer related are the input document to the cited document
PaperlD: 213 In addition to classic references, Scienstein analyses references that were added by users and that we call collaborative links'[14]. These links may, for instance, occur in collaborative annotations and can be classified as contradiction, correction, supporting, or addition/improvement (see Figure 4). In contrast to references, the links may point to publications that were published after the paper or were Document D unknown to the author and hence provide valuable information to the readers. For recommendation purposes, the links' classifications are important. With citation analysis based on classic references it can only be determined that two documents th classified collaborative input Document links it can be expressed how they are related Citing Document Figure 3: In-text Impact Index(ItIF) ICDa analyses the distance between references within a text to determine the degree of their similarity(see Figure 5 for illustration). The idea is that the more similar two documents are, the more likely they are closely referenced in Document 1 Document 2 other research papers. For Scienstein, the Distance Similarity Index(DSI) was developed, which calculates the similarity of two documents based on the citation distance. If two references occur in the same sentence. the referenced documents are likely to be very similar and the DsI is 1. If hey occur in the same paragraph the dsI is y2. The other values used are shown in Table 1 Smith [See Profile] AddErly DS|=% Ds|=1 I doubt this. several studies have shown that ref- Figure 5: Distance Similarity Index J. Cooper 1989, maton &J. Lewis 2001 and M.ohnson 2 authors a publications'qual doi: 10.1003/pbx713 alternative to subjective eval V SCIENSTEIN'S AUTHOR AND SOURCE ANALYSIS by J. Smith, a majority of authors honestly and carefully reference their influences [8]. As a To find further potentially relevant papers, Scienstein uses a Figure 4: Collaborative Annotations and Links simple, but nevertheless in practice unapplied method. Those First experiments with in-text frequency and distance papers are considered relevant that were published by the analysis delivered promising results. However, further same author or source(e.g. journal)as the input document research needs to be performed for optimizing the algorithms author and source analysis can be used to rank and for identifying the right weighting of variables, which seem to depend on the publications research field recommendations, for instance, by reputation. In Scienstein, the user can decide the way reputation is measured. Besides Table 1: Distance values common standards such as the impact factor or h-index, users can define the way reputation is measured themselves, e.g. by Occurrence Value Occurrence Value implicit and explicit ratings or combinations entence Paragraph1/2 Other Section
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Citation analysis is an objective measure for authors’ and publications’ quality and the best alternative to subjective evaluations. As shown by J. Smith, a majority of authors honestly and carefully reference their influences [8]. As a by J. Smith, a majority of authors honestly and I doubt this. Several studies have shown that ref- erence are often incorrect due to the matthew effect, self-citations, citation circles, and so on. For instan- ce, J. Cooper 1989, H. Dalton & J. Lewis 2001 and M. Johnson 2006. Mark Smith [See Profile] Add Reply carefully reference their influences [8] As shown [Contradicting Reference] doi:10.1003/ptx.713 X Figure 4: Collaborative Annotations and Links First experiments with in-text frequency and distance analysis delivered promising results. However, further research needs to be performed for optimizing the algorithms and for identifying the right weighting of variables, which seem to depend on the publication’s research field. Table 1: Distance Values Occurrence Value Occurrence Value Sentence 1 Chapter 1/8 Paragraph 1/2 Other 1/16 Section 1/4 In addition to classic references, Scienstein analyses references that were added by users and that we call ‘collaborative links’ [14]. These links may, for instance, occur in collaborative annotations and can be classified as contradiction, correction, supporting, or addition/improvement (see Figure 4). In contrast to references, the links may point to publications that were published after the paper or were unknown to the author and hence provide valuable information to the readers. For recommendation purposes, the links’ classifications are important. With citation analysis based on classic references it can only be determined that two documents are related somehow. With classified collaborative links it can be expressed how they are related. This is an example text with references to different documents. Another example. 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Besides common standards such as the impact factor or h-index, users can define the way reputation is measured themselves, e.g. by implicit and explicit ratings or combinations
PaperlD: 213 Author(s) and Explicit ratings provide the satisfaction of having ones own Source opinion voiced and valued Even if only very few users participate in the explicit rating of research papers, we believe that these ratings still deliver valuable information complementing the other approaches Figure 6: Author and Source Based Analysis Table 2: Actions monitored in document Usage mining VI. SCIENSTEINS TEXT MINING View document Details With regard to text mining, Scienstein basically uses sages in PDF existing techniques and only offers some additional features Bookmark Document such as the possibility of classifying papers according to View Coll. Annotations Add coll. annotations details given in the acknowledgements. This way, for instance Add Coll. Rating research projects supported by lobby groups can be easily identified if mentioned in the acknowledgements View Coll. Links Add Coll. Links Additionally, Scienstein considers data gathered by View Coll.Classifications Add Coll. Classifications collaborative annotations and classifications [14, 15] View Bibliography Send/recommend to friend Collaborative annotations are in-text comments made by the readers(see Figure orative classifications are similar to tags, but more structured. In the current prototype of Read Follow recommendations Scienstein, users can add tags in three main categories: field of View Related Docume Reference Document research. research methods and research details. In case of nterdisciplinary work for each category, several tags can be assigned. Additionally, further categories can be created. For In addition to explicit ratings, Scienstein generates implicit instance, it might be useful to classify publications about ratings by monitoring 22 user actions(see Table 2). We call archaeological sites according to their geographic location he process of monitoring the user's actions on a document This would allow the development of, for instance, a Google document usage mining. The underlying assumption is that Maps extension so that the user can zoom into sites to get intensively studied documents or paragraphs in documents are relevant publications listed more valuable for the user than documents that were. for The advantage of collaborative annotations and classifications is that new terms can retrospectively be example, closed after a few seconds associated to documents. For instance, Goldberg et al published in 1992 the idea of what we call today a recommender system [16]. However, the term recommender Based on document usage mining, Scienstein recommends system' was actually coined two years later by Resnick et al you the following papers Papers similar to the last papers you have read The delicate topic of the impact factor Why the impact factor of journals should not be used for VIL. SCIENSTEIN'S DOCUMENT RATING As explained, some authors consider collaborative filtering and explicit ratings as unsuitable for recommending research papers. However, we do not know of any studies supporting Papers ly published by you have read their assumptions. In contrast, we believe that for the majority Self-citations, co-authorships and keywords.A new approach of users, the costs of participating would be lower than the scientists field mobility benefits for the following reasons [18] Explicit ratings improve a users own recommendations □ Author口Year accuracy 口 Source口 Ratings口 Abstract Explicit ratings deliver document management functionality by serving as extended memory for a users preferences Figure 7: Document usage mining based recommendations Explicit ratings pleases a user by allowing him/her! to contribute to an advancing community
PaperID: 213 4 Input Document Document A Document B Author(s) and Source Figure 6: Author and Source Based Analysis VI. SCIENSTEIN’S TEXT MINING With regard to text mining, Scienstein basically uses existing techniques and only offers some additional features such as the possibility of classifying papers according to details given in the acknowledgements. This way, for instance, research projects supported by lobby groups can be easily identified, if mentioned in the acknowledgements. Additionally, Scienstein considers data gathered by collaborative annotations and classifications [14, 15]. Collaborative annotations are in-text comments made by the readers (see Figure 4). Collaborative classifications are similar to tags, but more structured. In the current prototype of Scienstein, users can add tags in three main categories: field of research, research methods and research details. In case of interdisciplinary work for each category, several tags can be assigned. Additionally, further categories can be created. For instance, it might be useful to classify publications about archaeological sites according to their geographic location. This would allow the development of, for instance, a Google Maps extension so that the user can zoom into sites to get relevant publications listed. The advantage of collaborative annotations and classifications is that new terms can retrospectively be associated to documents. For instance, Goldberg et al. published in 1992 the idea of what we call today a recommender system [16]. However, the term ‘recommender system’ was actually coined two years later by Resnick et al. [17]. VII. SCIENSTEIN’S DOCUMENT RATING As explained, some authors consider collaborative filtering and explicit ratings as unsuitable for recommending research papers. However, we do not know of any studies supporting their assumptions. In contrast, we believe that for the majority of users, the costs of participating would be lower than the benefits for the following reasons [18]. Explicit ratings improve a user’s own recommendations accuracy Explicit ratings deliver document management functionality by serving as extended memory for a user’s preferences Explicit ratings pleases a user by allowing him/her! to contribute to an advancing community Explicit ratings provide the satisfaction of having one's own opinion voiced and valued Even if only very few users participate in the explicit rating of research papers, we believe that these ratings still deliver valuable information complementing the other approaches. Table 2: Actions monitored in Document Usage Mining View Document Details Edit Document Details Read Abstract Highlight passages in PDF Bookmark Document Create Bookmark within PDF View Coll. Annotations Add Coll. Annotations View Coll. Ratings Add Coll. Ratings View Coll. Links Add Coll. Links View Coll. Classifications Add Coll. Classifications View Bibliography Send/Recommend to friend Download Print Read Follow Recommendations View Related Documents Reference Document In addition to explicit ratings, Scienstein generates implicit ratings by monitoring 22 user actions (see Table 2). We call the process of monitoring the user’s actions on a document ‘document usage mining’. The underlying assumption is that intensively studied documents or paragraphs in documents are more valuable for the user than documents that were, for example, closed after a few seconds. Based on document usage mining, Scienstein recommends you the following papers: Papers similar to the last papers you have read The delicate topic of the impact factor Why the impact factor of journals should not be used for evaluating research Impact Factor: Good Reasons for Concern more... Papers recently published by authors you have read Self-citations, co-authorships and keywords - A new approach to scientists’ field mobility Profiling citation impact - A new methodology more... Title Author Year Source Ratings Abstract Update M. Szklo (2008), Epidemiology, vol. 19, no. 3 Figure 7: Document usage mining based recommendations
PaperlD: 213 For generating implicit ratings and recommendations(see B. Relevance Selection Figure 7), Scienstein weighs each activity based on the user's Three possibilities exist in Scienstein to build up'search past behaviour. For instance, some users print every potential queries. First, an ordinary keyword-based search using relevant document, whereas others only print documents after Boolean operators can be performed. Secondly, a research careful inspection. In the latter case, the activity print would paper can be uploaded to perform a keyword and reference be assigned a higher weight analysis. The third possibility is to receive recommendations y entering arbitrary text and references into a text box and marking relevant and irrelevant content by a red or green VIIL. USER iNTERFACE virtual highlighter(see Figure 9) To support the user in managing the information flood Relevance selection resulting from the various technical possibilities, Scienstein 回冈 set store by the development of a user-friendly GUl. A d expectancy theories [I ll For instance, value selection of important concepts is presented in the following heories [12] consider that people value different A. Cockpit view included in any of the three integrated mode The Cockpit View' is the core of the Scienstein Taylorism [13] and Herzberg,'s two-far recommender GUl(see Figure 9). It consists of the graphical representation of recommended documents, various controls to ● vant O relevant [ Cancel[OK filter them and a context dependent legend The graphical view shows the recommended documents whereas the size of the displayed documents depends on the degree they fulfill the settings made on the right If desired, the approaches can be combined for further Recommended documents are grouped according to their filtering. The screenshot below illustrates this method.The classification. The classification is based on the journal, green-marked keywords and references are then included and keyword analysis, reference analysis and tags assigned by the red-marked content is excluded from recommendations users. By moving the mouse over a document, further information is displayed in a yellow box. Besides obtaining a C. Project Selection summary of relevant information the user has the possibility to rate the document or write annotations. If a document is Researchers often work on different projects at the same me and hence need recommendations in different fields in positively rated by clicking on the green check mark it will be the case of explicit and implicit ratings, a recommender marked as relevant and similar documents are recommended system needs to consider under which circums by being immediately enlarge Cockpit View Graphical View(relevant documents are larger) Publication date between 2002and2008 ng Collaborative R erlocker. JA Konstan G Terveen and J Select languages Data Mining 2006. Journal of Science and Recommenders(IF 3.2) Publication types Abstract: Recommender systems have been evaluated in many often incomparable, ways, In this paper we review the key en Relevance decisions in evaluating collaborative filtering recommender systems. More 75 Collaborative rating 2 Recomme Topicality Unrat 2)24(46(68 Collaborative. 2002002004200520062007 ear setting Change Query Server connection with Soensteinorg established Data processing completed Figure 8: Cockpit View
PaperID: 213 5 Figure 8: Cockpit View For generating implicit ratings and recommendations (see Figure 7), Scienstein weighs each activity based on the user’s past behaviour. For instance, some users print every potential relevant document, whereas others only print documents after careful inspection. In the latter case, the activity ‘print’ would be assigned a higher weight. VIII. USER INTERFACE To support the user in managing the information flood resulting from the various technical possibilities, Scienstein set store by the development of a user-friendly GUI. A selection of important concepts is presented in the following. A. Cockpit view The ‘Cockpit View’ is the core of the Scienstein recommender GUI (see Figure 9). It consists of the graphical representation of recommended documents, various controls to filter them and a context dependent legend. The graphical view shows the recommended documents, whereas the size of the displayed documents depends on the degree they fulfill the settings made on the right. Recommended documents are grouped according to their classification. The classification is based on the journal, keyword analysis, reference analysis and tags assigned by users. By moving the mouse over a document, further information is displayed in a yellow box. Besides obtaining a summary of relevant information the user has the possibility to rate the document or write annotations. If a document is positively rated by clicking on the green check mark it will be marked as relevant and similar documents are recommended by being immediately enlarged. B. Relevance Selection Three possibilities exist in Scienstein to build up ‘search queries‘. First, an ordinary keyword-based search using Boolean operators can be performed. Secondly, a research paper can be uploaded to perform a keyword and reference analysis. The third possibility is to receive recommendations by entering arbitrary text and references into a text box and marking relevant and irrelevant content by a red or green virtual highlighter (see Figure 9). Figure 9: Relevance selection If desired, the approaches can be combined for further filtering. The screenshot below illustrates this method. The green-marked keywords and references are then included and the red-marked content is excluded from recommendations. C. Project Selection Researchers often work on different projects at the same time and hence need recommendations in different fields. In the case of explicit and implicit ratings, a recommender system needs to consider under which circumstances
PaperlD: 213 respectively during which project a rating was performed. [5] Fano, R. M. 1956. Information theory and the retrieval of Therefore, different projects can be defined and new ones can ecorded information, in Documentation in Action, Shera, J H be derived from existing ones. These profiles can be published Kent, A. Perry, J. W.(Edts), New York: Reinhold Publ. Co., pp to assist other researchers in finding relevant literature(see Figure 10) Marshakova, 1. V. 1973. System of document connections based on references, Nauchno-Tekhnicheskaya Informatsiya, vol. 2 no.6,pp.3-8 Select Project [7 Small, H. 1973. Co-citation in the scientific literature: a new measure of the relationship between two documents, Journal of the American Society for Information Science, vol 24, pp. 265- Collaborative Document Evaluation recommender Systems- State of the Art [8]Giles, C. L. Bollacker, K. D. And Lawrence, S. 1998. Cite Seer: an automatic citation indexing system, In Digital Libraries 98 The Third ACM C New Project Cancel I「ok Meho, L 1. 2006. The Rise and Rise of Citation Analysis, lew Projects can be based on existing ones. I PhysicsWorldhttp:/larxivorg/abs/physics/0701012 Figure 10: Project Selection [10 Merton, RK 1968. The Matthew Effect in Science, Science, vol.159,no.3810,pp.56-63 IX. CONCLUSION [11 Tagliacozzo, R. 1977. Self-citations in scientific literature, Journal of Documentation, vol. 33, no 4, pp 251-265 In this paper, Scienstein, the first hybrid recommender [12] Garfield, E and Welljams-Dorof, A. 1992. Citation data: their system for research papers, was introduced. Scienstein aims to use as quantitative indicators for science and technology be a powerful alternative to academic search engines by not evaluation and policy-making, Science Public Policy, vol. 19. no.5,pp.321-327 solely relying on keyword analysis, but by additionally using [13] Agarwal,N Haque, E. Liu, H and Parsons, L.2005.Research citation analysis, explicit ratings, implicit ratings, author analysis, and source analysis. Although some of the utilized Paper Recommender Systems: A Subspace Clustering methods have been known for decades, they have not been Approach, In Advances in Web-Age Information Management, Springer: Heidelberg applied in the context of research paper recommender systems. [14] Beel, J. and Gipp, B. 2008. Collaborative Document Evaluation Other approaches such as the in-text distance similarity index' or collaborative annotations classifications and links An Alternative Approach to Classic Peer Review. In Proceedings of World Academy of Science, Engineering and were developed exclusively for Scienstein. The combination Technology, vol 31, pp 410-413 of all approaches is critical since each approach possesses [15] Beel, J and Gipp, B. 2008. The Potential of Collaborativ disadvantages that can only be overcome by combining them. Document Evaluation for Science, the 1 I th Intermationeyc However, many questions remain unanswered, for instance Conference on Digital Asian Libraries (ICADL 2008), published garding non-technical aspects like privacy concerns nG. Buchanan, M. Masoodian S Cunningham(Eds resulting from implicit and explicit ratings. Further research in Digital Libraries: Universal and Ubiquitous Access to this field will be performed by the Scienstein team, which Information of Lecture Notes in Computer Science, vol. 5362, DOI10.1007/978-3-540-89533-6,ISSN03029743,pp.375- welcomes other researchers to participate 378, Springer-Verlag Berlin, Heidelberg Communications of the ACM. vol. 35, no 12, pp 61-70.o [16]Goldberg, D. Nichols, D. Oki, B M. and Terry, D. 1992. Us Collaborative Filtering to Weave an Information Tapestry X. REFERENCES [17 Resnick, P lacovo, N. Suchak, M. Bergstrom, P and riedl, J 1994. GroupLens: An open architecture for collaborative [1 May, R. M. 1997. The Scientific Wealth of Nations, Science filtering of Netnews, in Proc. ACM conference on Computer vol.275,n.5301,pp.793-796 supported cooperative work, Chapel Hill, North Carolina United States ACM Press 2 Torres, R McNee, S M. Abel, M. Konstan, J. A. and Riedl,J 2004. Enhancing Digital Libraries with TechLens, Proceedings [18] Harper, F. Li, X Chen, Y and Konstan, J. 2005. An Economic of JCDL 04, pp 228-236 Model Of User Rating In An Online Recommender System, in Proceedings of the 10th International Conference on User 3 Pennock, D M. Horvitz, E. Lawrence, S and Giles, L C 2000 Modeling, Edinburgh, UK Collaborative Filtering by Personality Diagnosis: A Hybrid Memory-and Model-Based Approach, in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligen 4 Middleton, S.E. Shadbolt, N.R. and De roure, D. C. 2004 Ontological User Profiling in Recommender Systems, ACM Transactions on Information Systems(TOIS), voL. 22, no 1, pp
PaperID: 213 6 respectively during which project a rating was performed. Therefore, different projects can be defined and new ones can be derived from existing ones. These profiles can be published to assist other researchers in finding relevant literature (see Figure 10). Figure 10: Project Selection IX. CONCLUSION In this paper, Scienstein, the first hybrid recommender system for research papers, was introduced. Scienstein aims to be a powerful alternative to academic search engines by not solely relying on keyword analysis, but by additionally using citation analysis, explicit ratings, implicit ratings, author analysis, and source analysis. Although some of the utilized methods have been known for decades, they have not been applied in the context of research paper recommender systems. Other approaches such as the ‘in-text distance similarity index’ or collaborative annotations, classifications and links were developed exclusively for Scienstein. The combination of all approaches is critical since each approach possesses disadvantages that can only be overcome by combining them. However, many questions remain unanswered, for instance regarding non-technical aspects like privacy concerns resulting from implicit and explicit ratings. Further research in this field will be performed by the Scienstein team, which welcomes other researchers to participate. X. REFERENCES [1] May, R. M. 1997. The Scientific Wealth of Nations, Science, vol. 275, no. 5301, pp. 793-796. [2] Torres, R. McNee, S. M. Abel, M. Konstan, J. A.. and Riedl, J. 2004. Enhancing Digital Libraries with TechLens, Proceedings of JCDL’04, pp. 228-236. [3] Pennock, D. M. Horvitz, E. Lawrence, S. and Giles, L. C. 2000. Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach, in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (San Francisco). [4] Middleton, S.E. Shadbolt, N. R. and De Roure, D. C. 2004. Ontological User Profiling in Recommender Systems, ACM Transactions on Information Systems (TOIS), vol. 22, no.1, pp. 54-88. [5] Fano, R. M. 1956. Information theory and the retrieval of recorded information, in Documentation in Action, Shera, J. H. Kent, A. Perry, J. W. (Edts), New York: Reinhold Publ. Co., pp. 238–244. [6] Marshakova, I. V. 1973. System of document connections based on references, Nauchno-Tekhnicheskaya Informatsiya, vol. 2, no. 6, pp. 3–8. [7] Small, H. 1973. Co-citation in the scientific literature: a new measure of the relationship between two documents, Journal of the American Society for Information Science, vol. 24, pp. 265– 269. [8] Giles, C. L. Bollacker, K. D. And Lawrence, S. 1998. CiteSeer: an automatic citation indexing system, In Digital Libraries 98 - The Third ACM Conference on Digital Libraries, pp. 89-98. [9] Meho, L. I. 2006. The Rise and Rise of Citation Analysis, Physics World, http://arxiv.org/abs/physics/0701012. [10] Merton, R. K 1968. The Matthew Effect in Science, Science, vol. 159, no. 3810, pp. 56-63. [11] Tagliacozzo, R. 1977. Self-citations in scientific literature, Journal of Documentation, vol. 33, no. 4, pp. 251-265. [12] Garfield, E. and Welljams-Dorof, A. 1992. Citation data: their use as quantitative indicators for science and technology evaluation and policy-making, Science & Public Policy, vol. 19, no. 5, pp. 321-327. [13] Agarwal, N. Haque, E. Liu, H. and Parsons, L. 2005. Research Paper Recommender Systems: A Subspace Clustering Approach, In Advances in Web-Age Information Management, Springer: Heidelberg. [14] Beel, J. and Gipp, B. 2008. Collaborative Document Evaluation: An Alternative Approach to Classic Peer Review. In Proceedings of World Academy of Science, Engineering and Technology, vol. 31, pp. 410-413. [15] Beel, J. and Gipp, B. 2008. The Potential of Collaborative Document Evaluation for Science, the 11th International Conference on Digital Asian Libraries (ICADL 2008), published in G. Buchanan, M. Masoodian & S. Cunningham (Eds.), Digital Libraries: Universal and Ubiquitous Access to Information of Lecture Notes in Computer Science, vol. 5362, DOI 10.1007/978-3-540-89533-6, ISSN 0302-9743, pp. 375- 378, Springer-Verlag Berlin, Heidelberg. [16] Goldberg, D. Nichols, D. Oki, B. M. and Terry, D. 1992. Using Collaborative Filtering to Weave an Information Tapestry, Communications of the ACM, vol. 35, no. 12, pp. 61-70. [17] Resnick, P. Iacovou, N. Suchak, M. Bergstrom, P. and Riedl, J. 1994. GroupLens: An open architecture for collaborative filtering of Netnews, in Proc. ACM conference on Computer supported cooperative work, Chapel Hill, North Carolina, United States ACM Press. [18] Harper, F. Li, X. Chen, Y. and Konstan, J. 2005. An Economic Model Of User Rating In An Online Recommender System, in Proceedings of the 10th International Conference on User Modeling, Edinburgh, UK
