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Recommending Scientific Articles Using CiteULike Toine Bog Antal van den bosch Po.Box90153.5000LE PO.Box90153.5000LE Tilburg, The Netherlands Tilburg, The Netherlands A M Bogers @uvt. nl Antal vdn Bosch @uvt. nl ABSTRACT pecial functionality for certain academic resources, such as linkin We describe the use of the social reference management website to online versions of papers and special access to metadata specific CiteULike for recommending scientific articles to users, based on to academic resources their reference library. We test three different collaborative filter All four mentioned bibliographical reference managers encour- ing algorithms, and find that user-based filtering performs best. A age users to organize their references wi temporal analysis of the data indexed by CiteULike shows that it keywords. These in turn enable users to view all references, fron takes about two years for the cold-start problem to disappear and any user, associated with a chosen tag, as well as information about the popularity of a reference. This same linking is also applied to the author level so that users can browse other users who added ref- erences to publications written by a specific author. These features Categories and Subject Descriptors an help users to better cope with the information overload that is H 3 [Information Storage and Retrieval]: H 3.4 Systems and as overwhelming in the academic community as it is on the Web, Software: H 3.5 Online Information Services: H.3.7 Digital with an ever-increasing number of journals, books, and conference proceedings being published every year. This overload makes it hard to keep up with interesting new work, or to get a complete General Terms overview of relevant literature on specific topics For these features to be effective, active use of the online sys Algorithms, Me tem on the part of the user(searching, browsing) is needed. Our interest lies in using recommender systems to relieve this burden 1. INTRODUCTION and automatically find interesting and related reading material for One of the trends within the Web 2.0 paradigm is a shift in infor the user. A recommender system is a type of personalized infor- mation access from local and solitary, to global and collaborative. mation filtering technology used to identify a sets of items that are likely to be of interest to a certain user. One particular class of rec- Instead of storing, managing, and accessing personal information ommendation algorithms is collaborative filtering(CF), that base on only one specific computer or browser, personal information nanagement and access has been moving more and more to the recommendations on the opinions or actions of other like-minded Web. Social bookmarking websites are clear cases in point: in users. The motivation here is that a user will be more satisfied with stead of keeping a local copy of pointers to favorite URLS,users recommended items that are liked by like-minded users, than by can instead store and access their bookmarks online through a web items that are picked randomly or based on overall popularity. interface. The underlying application then makes all stored infor In this paper, we focus on using one of these social reference nation sharable among users, allowing for improved searching and managers, CiteULike, to generate reading lists for scientific arti- generating recommendations between users with similar interests. construction of a test collection based on the services offered by A special kind of social bookmarking services-and the focus of Cite uLike and apply three different CF algorithms to our data. We our paper-are social reference managers such as CiteULike, Con notea,Bibsonomy, and 2Collab'that aid users in managing also analyze the data across its temporal dimension: we use pub- eference collection. All of these services allow users to bo licly available activity logs to determine how recommendation per- any Web page or reference they choose, and in addition they formance changes as the website grows over time The paper is structured as follows. We discuss related work in AvaIlableathttp://www.citeulike.org,http:// Section 2. We discuss CiteULike. how our test collection was cre- www.connotea.org,http://www.bibsonomy.organd d, and what issues we ran into in greater detail in Section 3 http://www.2collab.comrespectively we describe our experimental setup and evaluation, followed by the results in Section 5. Section 6 contains the results of our temporal analysis of the different algorithms. We conclude in Section 7 and highlight possible future work. Permission to make digital or hard copies of all or part of this work for not made or distributed for profit or commercial advantage and that copie 2. RELATED WORK bear this notice and the full citation on the first page. To copy otherwise, to Most of the work related to recommending interesting informa- permisosh, to post on servers or to redistribute to lists, requires prior specific RecSys08. October 23-25. 2008. Lausanne Switzerland. creating information management agents. Maes(1997)was among Copyright2008ACM978-1-60558-093-7/08/10…S500 the first to signal the need for information filtering agents that can

Recommending Scientific Articles Using CiteULike Toine Bogers ILK, Tilburg University P.O. Box 90153, 5000 LE Tilburg, The Netherlands A.M.Bogers@uvt.nl Antal van den Bosch ILK, Tilburg University P.O. Box 90153, 5000 LE Tilburg, The Netherlands Antal.vdnBosch@uvt.nl ABSTRACT We describe the use of the social reference management website CiteULike for recommending scientific articles to users, based on their reference library. We test three different collaborative filter￾ing algorithms, and find that user-based filtering performs best. A temporal analysis of the data indexed by CiteULike shows that it takes about two years for the cold-start problem to disappear and recommendation performance to improve. Categories and Subject Descriptors H.3 [Information Storage and Retrieval]: H.3.4 Systems and Software; H.3.5 Online Information Services; H.3.7 Digital Li￾braries General Terms Algorithms, Measurement, Performance, Experimentation 1. INTRODUCTION One of the trends within the Web 2.0 paradigm is a shift in infor￾mation access from local and solitary, to global and collaborative. Instead of storing, managing, and accessing personal information on only one specific computer or browser, personal information management and access has been moving more and more to the Web. Social bookmarking websites are clear cases in point: in￾stead of keeping a local copy of pointers to favorite URLs, users can instead store and access their bookmarks online through a Web interface. The underlying application then makes all stored infor￾mation sharable among users, allowing for improved searching and generating recommendations between users with similar interests. A special kind of social bookmarking services—and the focus of our paper—are social reference managers such as CiteULike, Con￾notea, Bibsonomy, and 2Collab1 that aid users in managing their reference collection. All of these services allow users to bookmark any Web page or reference they choose, and in addition they offer 1Available at http://www.citeulike.org, http:// www.connotea.org, http://www.bibsonomy.org, and http://www.2collab.com respectively. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. RecSys’08, October 23–25, 2008, Lausanne, Switzerland. Copyright 2008 ACM 978-1-60558-093-7/08/10 ...$5.00. special functionality for certain academic resources, such as linking to online versions of papers and special access to metadata specific to academic resources. All four mentioned bibliographical reference managers encour￾age users to organize their references with one or more tags, or keywords. These in turn enable users to view all references, from any user, associated with a chosen tag, as well as information about the popularity of a reference. This same linking is also applied to the author level, so that users can browse other users who added ref￾erences to publications written by a specific author. These features can help users to better cope with the information overload that is as overwhelming in the academic community as it is on the Web, with an ever-increasing number of journals, books, and conference proceedings being published every year. This overload makes it hard to keep up with interesting new work, or to get a complete overview of relevant literature on specific topics. For these features to be effective, active use of the online sys￾tem on the part of the user (searching, browsing) is needed. Our interest lies in using recommender systems to relieve this burden and automatically find interesting and related reading material for the user. A recommender system is a type of personalized infor￾mation filtering technology used to identify a sets of items that are likely to be of interest to a certain user. One particular class of rec￾ommendation algorithms is collaborative filtering (CF), that base recommendations on the opinions or actions of other like-minded users. The motivation here is that a user will be more satisfied with recommended items that are liked by like-minded users, than by items that are picked randomly or based on overall popularity. In this paper, we focus on using one of these social reference managers, CiteULike, to generate reading lists for scientific arti￾cles based on a user’s online reference library. We describe the construction of a test collection based on the services offered by CiteULike and apply three different CF algorithms to our data. We also analyze the data across its temporal dimension: we use pub￾licly available activity logs to determine how recommendation per￾formance changes as the website grows over time. The paper is structured as follows. We discuss related work in Section 2. We discuss CiteULike, how our test collection was cre￾ated, and what issues we ran into in greater detail in Section 3. In the following Section 4 we describe our experimental setup and evaluation, followed by the results in Section 5. Section 6 contains the results of our temporal analysis of the different algorithms. We conclude in Section 7 and highlight possible future work. 2. RELATED WORK Most of the work related to recommending interesting informa￾tion with respect to the user’s current interest or task has focused on creating information management agents. Maes (1997) was among the first to signal the need for information filtering agents that can

reduce overload [8]. Since then, several types of agents have been Temporal metadata such as the year and, if available, month of rototyped and developed for many different fields, such as the the article's publication. Web for a comprehensive overview of agents available on the Web Miscellaneous metadata such as the article The extracted Yet there have been only a handful of approaches to recommend- data also includes the publisher details and number ing interesting academic articles to users, MeNee et al. (2006)ar information, and the number of pages SSN/ISBN uably being the most prominent one. McNee frames the interac- identifiers were also extracted as well volume ar tion between users and recommender systems, focusing on recom- the online reabouts of the article mending interesting research papers from a user-centric perspec- User-specific metadata including the tags assigned by each user tive. He identifies the different tasks a recommender system coul perform to assist the user, such as finding a starting point for re- comments by users on an article, and reading priorities carch on a particular topic, and maintaining awareness of a re- search field. Recommendations are generated on the basis of cita- As CiteULike offers the possibility of users setting up groups that connect users that share similar academic and topical interests, tions in scientific papers [10] for each group we collected the group name, a short textual descrip- Basu et al. 2001) focus on the related problem of recommending tion and a list of its members conference paper submissions to reviewing committee members 1]. They use a content-based approach to paper recommendation, 3.2 Characteristics of the collection using the Vector Space model with tf-idf weighting. Another re- lated area of research is the development of recommender systems After crawling and data clean-up, our collection contained a total that employ folksonomies. Most of the work so far has focused f 1,012, 898 different postings, where we define a posting as a user item pair in the database, i.e. an item that was added to a CiteULike recommending tags for bookmarks. Jaschke et al. (2007), for in- user profile. These postings comprised 803, 521 unique articles stance, compared two different CF algorithms with a graph-based algorithm for recommending tags in Bibsonomy. They found that posted by 25, 375 unique users using 232, 937 unique tags. Meta- data was available for 543, 433 of the 803. 521 articles.CiteULike the top 3 ranks [6]. Mishne (2006) per Similar experimen en predicting tags associated with blog posts [11]. In our ex member of one or more groups, corresponding to 9.1% of all users. We did not crawl the full text of publications, but 33.7% of the periments we focus on CiteULike as the social reference manager. articles included the abstract in their metadata Capocci et al. analyze the small-world properties of the CiteULike folksonomy [21 4. RECOMMENDING USING CITEULIKE 3. CITEULIKE McNee identifies eight different tasks that a recommender sys- tem could fulfill in a digital library CiteULike is a website that offers a"a free service to help you these tasks are equally applicable in the CiteULike environment, to store, organise, and share the scholarly papers you are reading and not all of them can be fulfilled using the collection we created It allows its users to add their academic reference library to their However, a social reference manager could arguably fulfill addi- online profile on the CiteULike website. At the time of writing. tional, new tasks not applicable in a digital library environment. CiteULike contains around 885, 310 unique items, annotated by 27, 489 users with 174,322 unique tags. Articles can be stored In this paper we focus on the task of generating list of related pa their metadata(in various formats), abstracts, and links to the pa- pers based on a users reference library. This task corresponds most closely to McNee's tasks of Fill Out Reference Lists and Maintain pers at the publishers'websites. Users can also add reading prior Awareness [10]. In contrast to McNee's approach of using cita- ities, personal comments, and tags to their papers. CiteULike also tions, we use the direct user-item preference relations to generate our recommendations from nect users sharing academic or topical interests. These group pages report on recent actIvity offer the possibility of maintaining 4.1 Experimental setup discussion fora or blogs. The full text of articles is not accessible In order to evaluate different recommender algorithms on the from CiteULike. although links to online articles can be added CiteULike data and to compare the usefulness of the different infor 3.1 Constructing a test collection mation we have available, we need a proper framework for experi CiteULike offers daily dumps of their core database. We used mentation and evaluation. Recommender systems evaluation-and the differences with IR evaluation-have been addressed by, among the dump of November 2, 2007 as the basis for our experiments others, Herlocker et al. [4, 51, the latter identifying six discernible A dump contains all information on which articles were posted b recommendation tasks The recommendation task we evaluate here whom, with which tags, and at what point in time. It does no is the"Find Good Items"task", where users are provided with a however, contain any of the other metadata described above ranked list of recommended items, based on their personal profile crawled this metadata ourselves from the CiteULike website using Following common practice in recommender system evaluation the article IDs. We collected the following five types of metadata [4, 5, 10], to ensure that we would be able to generate reliable rec- ommendations. we select a realistic subset of the citeULike data Topic-related metadata including all metadata descriptive of th set by only keeping the users who have added 20 items rticle's topic, such as the title and the publication informa- the personal profile. In addition, we filter out all articles cur only once, since these items do not contain sufficiently Person-related metadata such as the authors of the article as well ties to the rest of the data set, and thus would only introduce noise as the editors of the journal or conference proceedings it was published in wehe spvemwhtimin maw wt de tee ar this is beyond ithe mecas tt nis pape 2seEhttp://www.citeulike.org/fag/data.adp 4 Also known as Top-N recommendation

reduce overload [8]. Since then, several types of agents have been prototyped and developed for many different fields, such as the Web, music, and academic writing. See Montaner et al. (2003) for a comprehensive overview of agents available on the Web. Yet there have been only a handful of approaches to recommend￾ing interesting academic articles to users, McNee et al. (2006) ar￾guably being the most prominent one. McNee frames the interac￾tion between users and recommender systems, focusing on recom￾mending interesting research papers from a user-centric perspec￾tive. He identifies the different tasks a recommender system could perform to assist the user, such as finding a starting point for re￾search on a particular topic, and maintaining awareness of a re￾search field. Recommendations are generated on the basis of cita￾tions in scientific papers [10]. Basu et al. (2001) focus on the related problem of recommending conference paper submissions to reviewing committee members [1]. They use a content-based approach to paper recommendation, using the Vector Space model with tf·idf weighting. Another re￾lated area of research is the development of recommender systems that employ folksonomies. Most of the work so far has focused on recommending tags for bookmarks. Jaschke et al. (2007), for in- ¨ stance, compared two different CF algorithms with a graph-based algorithm for recommending tags in Bibsonomy. They found that the graph-based algorithm outperforms the CF algorithms only for the top 3 ranks [6]. Mishne (2006) performs similar experiments when predicting tags associated with blog posts [11]. In our ex￾periments we focus on CiteULike as the social reference manager. Capocci et al. analyze the small-world properties of the CiteULike folksonomy [2]. 3. CITEULIKE CiteULike is a website that offers a “a free service to help you to store, organise, and share the scholarly papers you are reading”2 It allows its users to add their academic reference library to their online profile on the CiteULike website. At the time of writing, CiteULike contains around 885,310 unique items, annotated by 27,489 users with 174,322 unique tags. Articles can be stored with their metadata (in various formats), abstracts, and links to the pa￾pers at the publishers’ websites. Users can also add reading prior￾ities, personal comments, and tags to their papers. CiteULike also offers the possibility of users setting up and joining groups that con￾nect users sharing academic or topical interests. These group pages report on recent activity, and offer the possibility of maintaining discussion fora or blogs. The full text of articles is not accessible from CiteULike, although links to online articles can be added. 3.1 Constructing a test collection CiteULike offers daily dumps of their core database2 . We used the dump of November 2, 2007 as the basis for our experiments. A dump contains all information on which articles were posted by whom, with which tags, and at what point in time. It does not, however, contain any of the other metadata described above, so we crawled this metadata ourselves from the CiteULike website using the article IDs. We collected the following five types of metadata: Topic-related metadata including all metadata descriptive of the article’s topic, such as the title and the publication informa￾tion. Person-related metadata such as the authors of the article as well as the editors of the journal or conference proceedings it was published in. 2See http://www.citeulike.org/faq/data.adp. Temporal metadata such as the year and, if available, month of the article’s publication. Miscellaneous metadata such as the article type. The extracted data also includes the publisher details, volume and number information, and the number of pages. DOI and ISSN/ISBN identifiers were also extracted as well as URLs pointing to the online whereabouts of the article. User-specific metadata including the tags assigned by each user, comments by users on an article, and reading priorities. As CiteULike offers the possibility of users setting up groups that connect users that share similar academic and topical interests, for each group we collected the group name, a short textual descrip￾tion, and a list of its members. 3.2 Characteristics of the collection After crawling and data clean-up, our collection contained a total of 1,012,898 different postings, where we define a posting as a user￾item pair in the database, i.e. an item that was added to a CiteULike user profile. These postings comprised 803,521 unique articles posted by 25,375 unique users using 232,937 unique tags. Meta￾data was available for 543,433 of the 803,521 articles3 . CiteULike contained 1,243 different groups with 2,301 different users being a member of one or more groups, corresponding to 9.1% of all users. We did not crawl the full text of publications, but 33.7% of the articles included the abstract in their metadata. 4. RECOMMENDING USING CITEULIKE McNee identifies eight different tasks that a recommender sys￾tem could fulfill in a digital library environment [10]. Not all of these tasks are equally applicable in the CiteULike environment, and not all of them can be fulfilled using the collection we created. However, a social reference manager could arguably fulfill addi￾tional, new tasks not applicable in a digital library environment. In this paper we focus on the task of generating list of related pa￾pers based on a user’s reference library. This task corresponds most closely to McNee’s tasks of Fill Out Reference Lists and Maintain Awareness [10]. In contrast to McNee’s approach of using cita￾tions, we use the direct user-item preference relations to generate our recommendations from. 4.1 Experimental setup In order to evaluate different recommender algorithms on the CiteULike data and to compare the usefulness of the different infor￾mation we have available, we need a proper framework for experi￾mentation and evaluation. Recommender systems evaluation—and the differences with IR evaluation—have been addressed by, among others, Herlocker et al. [4, 5], the latter identifying six discernible recommendation tasks. The recommendation task we evaluate here is the “Find Good Items” task4 , where users are provided with a ranked list of recommended items, based on their personal profile. Following common practice in recommender system evaluation [4, 5, 10], to ensure that we would be able to generate reliable rec￾ommendations, we select a realistic subset of the CiteULike data set by only keeping the users who have added 20 items or more to the personal profile. In addition, we filter out all articles that oc￾cur only once, since these items do not contain sufficiently reliable ties to the rest of the data set, and thus would only introduce noise 3The overwhelming majority of the articles with missing metadata were spam articles. How we detected this is beyond the focus of this paper. 4Also known as Top-N recommendation

for our CF algorithms. This procedure generates a set of 258,701 in that neighborhood. A weighted aggregate of these frequencies user-item pairs, with 2, 539 unique users, and 87, 908 unique items. is used to generate the recommendations [4]. Item-based filtering When generating predictions, we withhold 10 randomly selected turns this around by matching items against the database to find the items from each user, and generate predictions by using the remain- neighborhood of similar items [131 ing data as training material. As retrieval algorithms recommender We test three different algorithms implemented in the freely avail algorithms tend to be controlled by several parameters. One way of able Suggest recommendation engine [7]. The first model is an optimizing these parameters is by maximizing performance on a item-based filtering approach that uses the cosine similarity met given data set. Such tuning. however, tends to overestimate the ex- ric to determine the similarity between items. Model 2 is an item- pected performance of the system, so in order to prevent this kind based algorithm that calculates similarity based on conditional prob- of overfitting we used 10-fold cross-validation [91 ability. Finally, model 3 is a user-based algorithm that uses the We first divided our data set into a training and a test set by ran sine similarity metric to determine similarity between users. Item- domly selecting 10% of the users to be in our test set. Final per- based filtering tends to perform well when there are more users than formance was evaluated on this 10% by withholding 10 items from items in the database whereas user-based filtering works better in each user, and using the remaining profile items together with the the reverse situation. We therefore expect the user-based filtering training set to generate the recommendations for those 10%0. In algorithm to outperform the other two algorithms order to properly optimize parameters we divided our training set ontaining 90% of the users) by randomly dividing the users over 5. RESULTS 10 folds, each containing 10% of the training users. Each fold is used as a validation set once with 10 items being withheld for eacl The CF algorithms we empl study have one impor- validation fold user. The remaining profile items and the data in the tant parameter that can be tuned: the neighborhood size k,i.e other 9 folds are then used to generate the predictions. The final the number of similar users used to generate the predictions. We values for our evaluation metrics on the withheld items were then tune this parameter for each of the models, using our 10-fold cross averaged over the 10 folds validation setup described in Section 4.1, varying k between I and 4.2 Evaluation effect of neighborhood size on the map scores of the three algo- rithms for k up to 140: MAP values stay the same for k>140. The In our evaluation, we adopt an iR perspective by treating each of other metrics show a similar trend over the different k values the users as a separate query or topic. The 10 withheld items for ach user make up the relevant items for which we have relevance udgments. For each user, a ranked list of items is produced and evaluated on whether these withheld items show up in the result model 2(ite model 3(user-based w/ cosine) list. While it is certainly possible and very likely that the recom- mendation lists contain other recommendations that the user would find relevant or interesting we cannot know this without the user judging them. This means that because our relevance judgment 02 correspond to items added to a user's profile, we can never have any items judged as being not relevant without user intervention Herlocker et al. [5] assesses the usefulness of different metrics for each of the six recommendation tasks they identified. For our Find Good Items"task, they find that metrics taking into account the ranking of the items are most appropriate. We therefore evalu- ated our recommender system using Mean Average Precision(MAP) Mean Reciprocal Rank(MRR) and Precision ( 10(P@10). In 20406080100120140 addition to these IR metrics, we also measured coverage: certain recommendation algorithms need enough data on a user or item for them to be able to reliably generate recommendations. Not all algo- Figure 1: The effect of neighborhood size k on MAP for the rithms will be able to cover every user or item. We therefore mea three algorithms. sure user coverage (UCOV), which is the percentage of all users for which we were able to generate any predictions at all. The two item-based algorithms, models I and 2, both have optimal k of 500. Performance increases for Models I and 2 as k 4.3 Alge orithm mender implementation ran out of ory when we tried increasing k beyond 500. The optimal value of pare three different CF algorithms. The term collaborative filter k for Model 3 lies between 4 and 8. with none of the differences was first used by Goldberg et al. [3] and describes a class of al in this range being statistically significantly different. We there- gorithms that, instead of looking at the content, use data about all fore picked a k of 5 as the optimal value for Model 3. Howeve for all values of k the user-based filtering algorithm significantly so-called'active'user. We use and briefly describe the two most outperforms the item-based filtering algorithm(P <10).Fi- user-based filtering, the active user is matched against the database Table 1. Model 3 outperforms the other models significantly find the neighboring users that the active user has a history of all measures(p< 10-6)and shows acceptable performance agreeing with. Once this neighborhood has been identified all ob preliminary approach jects in the neighbors' profiles unknown to the active user are con- sidered as possible recommendations and sorted by their frequen 6. TEMPORAL ANALYSIS CiteULike's daily database dumps offer us the unique opport SSee[14] for an overview of more sophisticated CF algorithms nity of analyzing the influence of growth of a social bookmarking

for our CF algorithms. This procedure generates a set of 258,701 user-item pairs, with 2,539 unique users, and 87,908 unique items. When generating predictions, we withhold 10 randomly selected items from each user, and generate predictions by using the remain￾ing data as training material. As retrieval algorithms, recommender algorithms tend to be controlled by several parameters. One way of optimizing these parameters is by maximizing performance on a given data set. Such tuning, however, tends to overestimate the ex￾pected performance of the system, so in order to prevent this kind of overfitting we used 10-fold cross-validation [9]. We first divided our data set into a training and a test set by ran￾domly selecting 10% of the users to be in our test set. Final per￾formance was evaluated on this 10% by withholding 10 items from each user, and using the remaining profile items together with the training set to generate the recommendations for those 10%. In order to properly optimize parameters we divided our training set (containing 90% of the users) by randomly dividing the users over 10 folds, each containing 10% of the training users. Each fold is used as a validation set once, with 10 items being withheld for each validation fold user. The remaining profile items and the data in the other 9 folds are then used to generate the predictions. The final values for our evaluation metrics on the withheld items were then averaged over the 10 folds. 4.2 Evaluation In our evaluation, we adopt an IR perspective by treating each of the users as a separate query or topic. The 10 withheld items for each user make up the relevant items for which we have relevance judgments. For each user, a ranked list of items is produced and evaluated on whether these withheld items show up in the result list. While it is certainly possible and very likely that the recom￾mendation lists contain other recommendations that the user would find relevant or interesting, we cannot know this without the user judging them. This means that because our relevance judgments correspond to items added to a user’s profile, we can never have any items judged as being not relevant without user intervention. Herlocker et al. [5] assesses the usefulness of different metrics for each of the six recommendation tasks they identified. For our “Find Good Items” task, they find that metrics taking into account the ranking of the items are most appropriate. We therefore evalu￾ated our recommender system using Mean Average Precision (MAP), Mean Reciprocal Rank (MRR) and Precision @ 10 (P@10). In addition to these IR metrics, we also measured coverage: certain recommendation algorithms need enough data on a user or item for them to be able to reliably generate recommendations. Not all algo￾rithms will be able to cover every user or item. We therefore mea￾sure user coverage (UCOV), which is the percentage of all users for which we were able to generate any predictions at all. 4.3 Algorithms In the preliminary experiments described in this paper we com￾pare three different CF algorithms. The term collaborative filtering was first used by Goldberg et al. [3] and describes a class of al￾gorithms that, instead of looking at the content, use data about all users’ preferences for items to generate recommendations for the so-called ‘active’ user. We use and briefly describe the two most simple variants: user-based filtering and item-based filtering5 . In user-based filtering, the active user is matched against the database to find the neighboring users that the active user has a history of agreeing with. Once this neighborhood has been identified, all ob￾jects in the neighbors’ profiles unknown to the active user are con￾sidered as possible recommendations and sorted by their frequency 5See [14] for an overview of more sophisticated CF algorithms. in that neighborhood. A weighted aggregate of these frequencies is used to generate the recommendations [4]. Item-based filtering turns this around by matching items against the database to find the neighborhood of similar items [13]. We test three different algorithms implemented in the freely avail￾able Suggest recommendation engine [7]. The first model is an item-based filtering approach that uses the cosine similarity met￾ric to determine the similarity between items. Model 2 is an item￾based algorithm that calculates similarity based on conditional prob￾ability. Finally, model 3 is a user-based algorithm that uses the co￾sine similarity metric to determine similarity between users. Item￾based filtering tends to perform well when there are more users than items in the database whereas user-based filtering works better in the reverse situation. We therefore expect the user-based filtering algorithm to outperform the other two algorithms. 5. RESULTS The CF algorithms we employ in our study have one impor￾tant parameter that can be tuned: the neighborhood size k, i.e. the number of similar users used to generate the predictions. We tune this parameter for each of the models, using our 10-fold cross￾validation setup described in Section 4.1, varying k between 1 and 500, evaluating performance at each step. Figure 1 displays the effect of neighborhood size on the MAP scores of the three algo￾rithms for k up to 140; MAP values stay the same for k > 140. The other metrics show a similar trend over the different k values. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 20 40 60 80 100 120 140 MAP k model 1 (item-based w/ cosine) model 2 (item-based w/ cond. prob.) model 3 (user-based w/ cosine) Figure 1: The effect of neighborhood size k on MAP for the three algorithms. The two item-based algorithms, models 1 and 2, both have an optimal k of 500. Performance increases for Models 1 and 2 as k increases, but the recommender implementation ran out of mem￾ory when we tried increasing k beyond 500. The optimal value of k for Model 3 lies between 4 and 8, with none of the differences in this range being statistically significantly different. We there￾fore picked a k of 5 as the optimal value for Model 3. However, for all values of k the user-based filtering algorithm significantly outperforms the item-based filtering algorithm (p < 10−10). Fi￾nal performance of the three models on the test set is displayed in Table 1. Model 3 outperforms the other models significantly on all measures (p < 10−6 ) and shows acceptable performance for a preliminary approach. 6. TEMPORAL ANALYSIS CiteULike’s daily database dumps offer us the unique opportu￾nity of analyzing the influence of growth of a social bookmarking

Table 1: Results of our three CF algorithms on the data set. interesting observation is that the optimal neighborhood size for Model 1 Model 2 Model 3 item-based filtering is five users. This corresponds rather well to the average group size of 4.8 on CiteULike MAP 0.1307 0.134 MRR0.26220.30040.3278 We also performed a temporal analysis on the data, and identified P@100.14120.14510.2524 a clear cold-start problem for CF algorithms on the CiteULike. The UCOV|9209%92.09%9960% results show that it took about two years for CF performance to start mproving to a useful level. An interesting question is whether this two year period is specific to CiteULike or that a similar pattern service such as CiteULike on recommendation performance. The bookmarking websites. This g these experiments on other social database dumps contain the time stamps for each posting: we used for future work these to construct growth curves of the different algorithms from In other future work, we plan to experiment with and combine temporal perspective. We gradually increased the size of our data several different recommender algorithms, based on the different and optimal parameters at each temporal step. The first item was set. Content-based algorithms should be able to take advantage of added to CiteULike on November 4, 2004, giving us 37 months of the full text of the documents and the metadata, while activity logs data. We repeated these steps 10 times and calculated the averag can be used to take recency effects into account. The CiteULike MAP scores folksonomy offers another promising avenue of contextual recom- Figure 2 shows the maP scores for the three different algorithms mendation material. The different algorithms based on the different contextual data will then be combined to determine the optimal rec- the 37 months, the user-based filtering method always outperforms ommendation process. Our end goal is to test our methods on users the other two item-based models. All CF algorithms achieve rel tively high MAP scores in the initial months, after which perfor- nance remains low and erratic for the first two years. The last 8. ACKNOWLEDGMENTS months of 2006 mark a turning point where a critical mass seems The work of Toine Bogers is funded by the IOP-MMI progr to have been reached. From that point on, recommendation perfor mance starts to climb and even triples in the span of one year for all of the A Propos project. Antal van den Bosch is funded by Nwo three algorithms. With the exception of the outliers in the first few the Netherlands Organization for Scientific Research onths. this seems to be a clear illustration of the cold-start prob lem CF algorithms suffer from at a system-wide level: it is hard 9. references to generate recommendations for new items in the start-up phase, when there is not enough usage data about new items to make reli- [1 C. Basu, H. Hirsh, able correlations with other items [5] Sources. Journal of Artificial Intelligence Research, 1: 231-252, 2001 [2] A. Capocci and G. Caldarelli. Folksonomies and Clustering in the Collaborative System CiteULike. eprint arXiv: 0710.2835, 2007 model 3 (user-based wi [3] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using Collabora- 0 the ACM,35(12):61-70,1992. [4] J. L Herlocker, J. A Konstan, A. Borchers, and J Riedl. An Algorith ings of S/ 99, pages 230-237, New York, NY, 1999. ACM. 5 J. L Herlocker, J. A. Konstan, L G. Terveen, and J. T. Riedl. Evalu 15 der Systems. ACM Transac ions on Information Systems, 22(1 ): 5-53. 2004. [ R. Jaschke, L. B. Marinho, A. Hotho, L. Schmidt-Thieme, and G. Stumme. Tag Recommendations in Folksonomies. In Proceedings 0.05 of PKDD 2007, volume 4702 of Lecture Notes in Computer Science, pages 506-514. Springer Verlag, 2007. 2005 2006 [7 G. Karypis. SUGGEST Top-N Recommendation Engine. Available fordownloadfromhttp://glaros.dtcumn.edu/gkhome/suggest/,2000 [8] P Maes. Agents that Reduce Work and Information Overload. Sofi. Figure 2: Performance on the citeUlike data set over time. [9] C. D. Manning. P. Raghavan, and H. Schultze. Introduction to Infor- ation Retrieval. Cambridge University Press, 2008 7. discussion FutUre worK tems. PhD thesis. University of Minnesota, June 2006 In this paper we demonstrated how a social reference manager can be used as a test collection for recommending research pa Assignment for Weblog Posts. In Proceedings of www 06, 2006. ers. The data we collected represent many different aspects of [12] M. Montaner, B. Lopez, and J. L de la Rosa. A Taxonomy of Recom- both users and their annotations, and offers many opportunities for ts on the Internet. Artificial Intellig 85-330,2003 testing and combining different representations and recommender [13] B Sarwar, G. Karypis, J Konstan, and J Riedl. Analysis of Recom- algorithms. We found that a user-based filtering algorithm yields undation Algorithms for E-Commerce. In Proceedings of EC '00, erformance. The likely explanation data set contains a magnitude more items than users. which makes [14] L Si and R Jin. Flexible Mixture Model for Collaborative Filter it both harder and slower to perform item-based filtering. Another In Proceedings of ICML 03, pages 704-711 AAAl Press, 2003

Table 1: Results of our three CF algorithms on the data set. Model 1 Model 2 Model 3 MAP 0.1307 0.1344 0.2478 MRR 0.2622 0.3004 0.3278 P@10 0.1412 0.1451 0.2524 UCOV 92.09% 92.09% 99.60% service such as CiteULike on recommendation performance. The database dumps contain the time stamps for each posting; we used these to construct growth curves of the different algorithms from a temporal perspective. We gradually increased the size of our data set of user-item pairs by a month at a time and used the same setup and optimal parameters at each temporal step. The first item was added to CiteULike on November 4, 2004, giving us 37 months of data. We repeated these steps 10 times and calculated the average MAP scores. Figure 2 shows the MAP scores for the three different algorithms plotted against the months in the CiteULike data set. Throughout the 37 months, the user-based filtering method always outperforms the other two item-based models. All CF algorithms achieve rel￾atively high MAP scores in the initial months, after which perfor￾mance remains low and erratic for the first two years. The last months of 2006 mark a turning point where a critical mass seems to have been reached. From that point on, recommendation perfor￾mance starts to climb and even triples in the span of one year for all three algorithms. With the exception of the outliers in the first few months, this seems to be a clear illustration of the cold-start prob￾lem CF algorithms suffer from at a system-wide level: it is hard to generate recommendations for new items in the start-up phase, when there is not enough usage data about new items to make reli￾able correlations with other items [5]. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 2005 2006 2007 2008 MAP date model 1 (item-based w/ cosine) model 2 (item-based w/ cond. prob.) model 3 (user-based w/ cosine) Figure 2: Performance on the CiteULike data set over time. 7. DISCUSSION & FUTURE WORK In this paper we demonstrated how a social reference manager can be used as a test collection for recommending research pa￾pers. The data we collected represent many different aspects of both users and their annotations, and offers many opportunities for testing and combining different representations and recommender algorithms. We found that a user-based filtering algorithm yields the best performance. The likely explanation for this is that the data set contains a magnitude more items than users, which makes it both harder and slower to perform item-based filtering. Another interesting observation is that the optimal neighborhood size for item-based filtering is five users. This corresponds rather well to the average group size of 4.8 on CiteULike. We also performed a temporal analysis on the data, and identified a clear cold-start problem for CF algorithms on the CiteULike. The results show that it took about two years for CF performance to start improving to a useful level. An interesting question is whether this two year period is specific to CiteULike or that a similar pattern would emerge when repeating these experiments on other social bookmarking websites. This is an important and interesting point for future work. In other future work, we plan to experiment with and combine several different recommender algorithms, based on the different contextual and metadata information present in the CiteULike data set. Content-based algorithms should be able to take advantage of the full text of the documents and the metadata, while activity logs can be used to take recency effects into account. The CiteULike folksonomy offers another promising avenue of contextual recom￾mendation material. The different algorithms based on the different contextual data will then be combined to determine the optimal rec￾ommendation process. Our end goal is to test our methods on users for one or two of the tasks identified by McNee et al. [10]. 8. ACKNOWLEDGMENTS The work of Toine Bogers is funded by the IOP-MMI program of SenterNovem / The Dutch Ministry of Economic Affairs, as part of the A Propos project. Antal van den Bosch is funded by NWO, ` the Netherlands Organization for Scientific Research. 9. REFERENCES [1] C. Basu, H. Hirsh, W. W. Cohen, and C. Nevill-Manning. Technical Paper Recommendation: A Study in Combining Multiple Information Sources. Journal of Artificial Intelligence Research, 1:231–252, 2001. [2] A. Capocci and G. Caldarelli. Folksonomies and Clustering in the Collaborative System CiteULike. eprint arXiv: 0710.2835, 2007. [3] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using Collabora￾tive Filtering to Weave an Information Tapestry. Communications of the ACM, 35(12):61–70, 1992. [4] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An Algorith￾mic Framework for Performing Collaborative Filtering. In Proceed￾ings of SIGIR ’99, pages 230–237, New York, NY, 1999. ACM. [5] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evalu￾ating Collaborative Filtering Recommender Systems. ACM Transac￾tions on Information Systems, 22(1):5–53, 2004. [6] R. Jaschke, L. B. Marinho, A. Hotho, L. Schmidt-Thieme, and ¨ G. Stumme. Tag Recommendations in Folksonomies. In Proceedings of PKDD 2007, volume 4702 of Lecture Notes in Computer Science, pages 506–514. Springer Verlag, 2007. [7] G. Karypis. SUGGEST Top-N Recommendation Engine. Available for download from http://glaros.dtc.umn.edu/gkhome/suggest/, 2000. [8] P. Maes. Agents that Reduce Work and Information Overload. Soft￾ware Agents, pages 145–164, 1997. [9] C. D. Manning, P. Raghavan, and H. Schutze. ¨ Introduction to Infor￾mation Retrieval. Cambridge University Press, 2008. [10] S. McNee. Meeting User Information Needs in Recommender Sys￾tems. PhD thesis, University of Minnesota, June 2006. [11] G. Mishne. AutoTag: A Collaborative Approach to Automated Tag Assignment for Weblog Posts. In Proceedings of WWW ’06, 2006. [12] M. Montaner, B. Lopez, and J. L. de la Rosa. A Taxonomy of Recom- ´ mender Agents on the Internet. Artificial Intelligence Review, 19(4): 285–330, 2003. [13] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Analysis of Recom￾mendation Algorithms for E-Commerce. In Proceedings of EC ’00, pages 158–167, New York, NY, 2000. ACM. [14] L. Si and R. Jin. Flexible Mixture Model for Collaborative Filtering. In Proceedings of ICML ’03, pages 704–711. AAAI Press, 2003