Abstract
Content recommender systems have become an invaluable tools in online communities where a huge volume of content items are generated for users to consume, making it difficult for users to find interesting content. Many recommender systems leverage articulated social networks or profile information (e.g, user background, interest, etc.) for content recommendation. These recommenders largely ignore the implied networks defined through user interactions. Yet these play an important role in formulating users’ common interests. We propose an interaction based content recommender which leverages implicit user interactions to determine the relationship trust or strength, generating a richer, more informed implied network. An offline analysis on a 5000 person, 12 week dataset from an online community shows that our approach outperforms algorithms which focus on articulated networks that do not consider relationship trust or strength.
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References
Adali, S., Escriva, R., Goldberg, M.K., Hayvanovych, M., Magdon-Ismail, M., Szymanski, B.K., Wallace, W.A., Williams, G.: Measuring behavioral trust in social networks. In: 2010 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 150–152 (2010)
Berkovsky, S., Freyne, J., Smith, G.: Personalized network updates: increasing social interactions and contributions in social networks. In: Masthoff, J., Mobasher, B., Desmarais, M.C., Nkambou, R. (eds.) UMAP 2012. LNCS, vol. 7379, pp. 1–13. Springer, Heidelberg (2012)
Brindal, E., Freyne, J., Saunders, I., Berkovsky, S., Noakes, M.: Weight tracking is predictive of weight loss for overweight/obese participants in a purely web-based intervention. J. Med. Internet Res (to appear, 2013)
Chen, J., Geyer, W., Dugan, C., Muller, M., Guy, I.: Make new friends, but keep the old: recommending people on social networking sites. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, pp. 201–210. ACM (2009)
Facebook Statistics, http://www.facebook.com/press/info.php?statistics (accessed December 2011)
Freyne, J., Berkovsky, S., Daly, E.M., Geyer, W.: Social networking feeds: recommending items of interest. In: Proceedings of the Fourth ACM Conference on Recommender Systems, Barcelona, Spain, pp. 277–280. ACM (2010)
Freyne, J., Jacovi, M., Guy, I., Geyer, W.: Increasing engagement through early recommender intervention. In: Proceedings of the Third ACM Conference on Recommender Systems, New York, USA, pp. 85–92. ACM (2009)
Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, pp. 211–220. ACM (2009)
Golbeck, J.: Trust on the world wide web: a survey. Foundations and Trends in Web Science 1, 131–197 (2006)
Guy, I., Ur, S., Ronen, I., Perer, A., Jacovi, M.: Do you want to know?: recommending strangers in the enterprise. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, Hangzhou, China, pp. 285–294. ACM (2011)
Kim, Y.A.: Building a web of trust without explicit trust ratings. In: 2008 IEEE 24th International Conference on Data Engineering Workshop, pp. 531–536 (2008)
Liu, H., Lim, E.-P., Lauw, H.W., Le, M.-T., Sun, A., Srivastava, J., Kim, Y.A.: Predicting trusts among users of online communities: an epinions case study. In: Proceedings of the 9th ACM Conference on Electronic Commerce, EC 2008, pp. 310–319. ACM, New York (2008)
Liu, H., Maes, P.: Interestmap: Harvesting social network profiles for recommendations. In: Beyond Personalization-IUI (2005)
Maheswaran, M., Tang, H.C., Ghunaim, A.: Towards a gravity-based trust model for social networking systems. In: Proceedings of the International Conference on Distributed Computing Systems Workshops, p. 24. IEEE Computer Society, Los Alamitos (2007)
Nepal, S., Sherchan, W., Paris, C.: STrust: a trust model for Social Networks. In: IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 841–846 (2011)
Paek, T., Gamon, M., Counts, S., Chickering, D.M., Dhesi, A.: Predicting the Importance of Newsfeed Posts and Social Network Friends (2010)
Trifunovic, S., Legendre, F., Anastasiades, C.: Social trust in opportunistic networks. In: Proceeding of 2010 INFOCOM IEEE Conference on Computer Communications Workshops, pp. 1–6. IEEE (2010)
WeiHang, C., Singh, M.P.: Trust-based recommendation based on graph similarity. In: 13th AAMAS Workshop on Trust in Agent Societies (2010)
Zuo, Y., Hu, W.-C., O’Keefe, T.: Trust computing for social networking. In: Proceedings of the 2009 Sixth International Conference on Information Technology: New Generations, pp. 1534–1539. IEEE Computer Society, Washington, DC (2009)
Kleanthous, S., Dimitrova, V.: Analyzing Community Knowledge Sharing Behavior. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 231–242. Springer, Heidelberg (2010)
Paliouras, G.: Discovery of Web user communities and their role in personalization. User Model. User-Adapt. Interact. 22(1-2), 151–175 (2012)
Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Autonomous Agents and Multi-Agent Systems 16(1), 57–74 (2008)
Kincaid, J.: EdgeRank: The secret sauce that makes Facebook’s news feed tick (2010), http://techcrunch.com/2010/04/22/facebook-edgerank (retrieved from March 15, 2013)
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Nepal, S., Paris, C., Pour, P.A., Freyne, J., Bista, S.K. (2013). Interaction Based Content Recommendation in Online Communities. In: Carberry, S., Weibelzahl, S., Micarelli, A., Semeraro, G. (eds) User Modeling, Adaptation, and Personalization. UMAP 2013. Lecture Notes in Computer Science, vol 7899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38844-6_2
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DOI: https://doi.org/10.1007/978-3-642-38844-6_2
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