Recommending Items in Social Tagging Systems Using Tag and Time Information

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In this work we present a novel item recommendation ap- proach that aims at improving Collaborative Filtering (CF) in social tagging systems using the information about tags and time. Our algorithm follows a two-step approach, where in the first step a potentially interesting candidate item-set is found using user-based CF and in the second step this can- didate item-set is ranked using item-based CF. Within this ranking step we integrate the information of tag usage and time using the Base-Level Learning (BLL) equation com- ing from human memory theory that is used to determine the reuse-probability of words and tags using a power-law forgetting function. As the results of our extensive evaluation conducted on data- sets gathered from three social tagging systems (BibSonomy, CiteULike and MovieLens) show, the usage of tag-based and time information via the BLL equation also helps to improve the ranking and recommendation process of items and thus, can be used to realize an effective item recommender that outperforms two alternative algorithms which also exploit time and tag-based information.

Text of Recommending Items in Social Tagging Systems Using Tag and Time Information

  • 1. Social Personalisation Workshop @ HT141Recommending Items in Social TaggingSystems using Tag and Time InformationChristoph University of Technology, Austria. Christoph Trattner 1.9.2014 PUC, Chile

2. Social Personalisation Workshop @ HT142Emanuel Lacicelacic@know-center.atTUGAustriaPaul Seitlingerpaul.seitlinger@tugraz.atTUGAustria. Christoph Trattner 1.9.2014 PUC, ChileDenis Parradparra@ing.puc.clPUCChileThanks toDominik Kowalddkowald@know-center.atKnow-CenterAustria 3. Social Personalisation Workshop @ HT143What will this talk be about? Social tags Temporal usage patterns of social tags Recommending items in social tagging systems An equation derived from human memory theory. Christoph Trattner 1.9.2014 PUC, Chile 4. Social Personalisation Workshop @ HT144Problem:Predict/Recommend items in socialtagging systems people (might be)interested in to read. Christoph Trattner 1.9.2014 PUC, Chile 4 5. Social Personalisation Workshop @ HT145Why are we doing this?Basically, to help the user in exploring an overloadedinformation space more efficiently. Christoph Trattner 1.9.2014 PUC, Chile5 6. Social Personalisation Workshop @ HT146Current approaches out there?!... aaaa looot on the tag prediction problem...Marinho et al. (2012)...but relativly little on recommending items to people insocial tagging systems...L. Balby Marinho, A. Hotho, R. Jschke, A. Nanopoulos, S. Rendle, L. Schmidt-Thieme, G.Stumme, and P. Symeonidis. Recommender Systems for Social Tagging Systems. SpringerBriefsin Electrical and Computer Engineering. Springer, Feb. 2012.. Christoph Trattner 1.9.2014 PUC, Chile 7. Social Personalisation Workshop @ HT147Temporal Tag Usage PatternsUsually the interests of users drift over time and so doestheir tagging behaviorThe work of e.g., Zhang et al. (2012) shows that the timecomponent is important for social tagging Models the time component using an exponential functionEmpirical research on human memory (Anderson &Schooler, 1991) showed that the reuse-probability of aword (= tag) depends on its usage-frequency andrecency in the past Models the time component using a power function. Christoph Trattner 1.9.2014 PUC, Chile7 8. Social Personalisation Workshop @ HT148Which function fits better to model thedrift of interests in social taggingsystems?. Christoph Trattner 1.9.2014 PUC, Chile8 9. Social Personalisation Workshop @ HT14 Linear distribution with log-scale9Empirical Analysis: BibSonomy (1). Christoph Trattner 1.9.2014 PUC, Chile9on Y-axis exponential function Linear distribution with log-scaleon X- and Y-axes power function 10. Social Personalisation Workshop @ HT1410Empirical Analysis: BibSonomy (2). Christoph Trattner 1.9.2014 PUC, Chile10Exponential distributionR = 31%Power distributionR = 89% 11. Social Personalisation Workshop @ HT1411Our ApproachBase-Level learning (BLL) equation - part of ACT-Rmodel Anderson et al. (2004):In previous work we have shown that this equation can beused to build an effective tag recommenderKowald et al. (2014), Trattner et al. (2014)Adaption for item recommendation:. Christoph Trattner 1.9.2014 PUC, Chile11 12. Social Personalisation Workshop @ HT1412Previous research (tag prediction)Trattner, C., Kowald, D., Seitlinger, P., Kopeinik, S. and Ley, T.: Modeling Activation Processes in Human Memory toPredict the Reuse of Tags, Journal of Web Science, 2014. (under review)Kowald, D., Seitlinger, P., Trattner, C. and Ley, T.: Long Time No See: The Probability of Reusing Tags as a Function ofFrequency and Recency, In Proceedings of the ACM World Wide Web Conference (WWW 2014), ACM, New York, NY,2014.. Christoph Trattner 1.9.2014 PUC, Chile 13. Social Personalisation Workshop @ HT1413Our Approach (2)= CIRTT 2 main stepsFirst step: User-based Collaborative Filtering (CF) to getcandidate items of similar usersSecond step: Item-based CF to rank these candidate items usingthe BLL equation to integrate tag and timeinformation:. Christoph Trattner 1.9.2014 PUC, Chile13 14. Social Personalisation Workshop @ HT14IR metrics: nDCG@20, MAP@20, Recall@20, Diversity and14How does it perform?3 freely-available folksonomy datasets BibSonomy (~ 340,000 tag assignments) CiteULike (~ 100.000 tag assignments) MovieLens (~ 100.000 tag assignments)Original datasets (no p-core pruning) Doerfel et al. (2013)80/20 split (for each user 20% most recent bookmarks/postsin test-set, rest in training-set)User Coverage. Christoph Trattner 1.9.2014 PUC, Chile14 15. Social Personalisation Workshop @ HT1415Baseline Methods Most Popular (MP) User-based Collaborative Filtering (CF) Two alternative approaches based on tag and timeinformation Zheng et al. (2011) exponential function Huang et al. (2014) linear function(remember: our CIRTT uses a power function). Christoph Trattner 1.9.2014 PUC, Chile15 16. Social Personalisation Workshop @ HT1416Results: nDCG plots. Christoph Trattner 1.9.2014 PUC, Chile16CIRTT reaches the highest level of accuracy 17. Social Personalisation Workshop @ HT1417Results: Recall plots. Christoph Trattner 1.9.2014 PUC, Chile17CIRTT reaches the highest level of accuracy 18. Social Personalisation Workshop @ HT1418...ok thats basically it . Christoph Trattner 1.9.2014 PUC, Chile 19. Social Personalisation Workshop @ HT1419What are we currently working on? Christoph Trattner 1.9.2014 PUC, Chile 20. Social Personalisation Workshop @ HT1420Thank you!Christoph TrattnerEmail: ctrattner@know-center.atWeb: christophtrattner.infoTwitter: @ctrattnerSponsors:. Christoph Trattner 1.9.2014 PUC, Chile 21. Social Personalisation Workshop @ HT1421Code and FrameworkCode and framework: Christoph Trattner 1.9.2014 PUC, Chile21