Abstract
Social networks provide users with information about their friends, their activities, and their preferences. In this paper we study the effectiveness of movie recommendations computed from such communicated preferences. We present a set of social movie recommendation algorithms, which we implemented on top of the Facebook social network, and we compare their effectiveness in influencing user decisions. We also study the effect of showing users a justification for the recommendations, in the form of the profile pictures of the friends that caused the recommendation.
We show that social movie recommendations are generally accurate. Furthermore, 80% of the users that are undecided on whether to accept a recommendation are able to reach a decision upon learning of the identities of the users behind the recommendation. However, in 27% of the cases, they decide against watching the recommended movies, showing that revealing identities can have a negative effect on recommendation acceptance.
This Work was supported in Part by a Gift from Google, Inc.
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References
Abdul-Rahman, A., Hailes, S.: Supporting trust in virtual communities. In: Hawaii International Conference on System Sciences. Maui (2000)
Adomavicius, G., Tuzhili, A.: Towards the Next Gen. of Recommender Systems. IEEE Transactions on Knowledge and Data Engineering 17, 634–749 (2005)
Asch, S.E.: Opinions and social pressure. Scientific American 193, 31–35 (1955)
Burke, R.: Hybrid Recommender Systems. User Mod. and User-Adap 12, 331–370 (2002)
Centola, D.: The Spread of Behavior in an Online Social Network Experiment. Science 329, 1194–1197 (2010)
Durao, F., Dolog, P.: A Personalized Tag-Based Recommendation in Social Web Systems. In: Intelligent Web and Information Systems.CoRR (2012)
Key Facts Statistics, http://newsroom.fb.com/content/default.aspx?NewsAreaId=22
Golbeck, J.:Generating Predictive Movie Recommendations from Trust in Social Networks, iTrust (2006)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: ACM Conference on Computer Supported Cooperative Work, pp. 241–250. ACM, New York (2000)
Huang, Z., Zeng, D., Chen, H.: A Link Analysis Approach to Recommendation under Sparse Data. In: Americas Conference on Information Systems (2004)
Jøsang, A., Quattrociocchi, W., Karabeg, D.: Taste and Trust. In: Wakeman, I., Gudes, E., Jensen, C.D., Crampton, J. (eds.) Trust Management V. IFIP AICT, vol. 358, pp. 312–322. Springer, Heidelberg (2011)
Resnick, P., Lacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering. In: ACM Conference on Computer Supported Cooperative Work, New York (1994)
Ricci, F., Rokach L.: Recommender Systems Handbook. Springer, New York (2011)
Sarwar, B.M., Karypis, G., Onstan, J.A., Riedl, J.: Recommender Systems for Large-scale E-Commerce. In: ICCIT (2002)
Social Recommender Systems Methods and User Issues, http://hci.epfl.ch/teaching/advanced-hci/slides/2011.5.23_Yu.pdf
Smith, B., Briggs, P., Coyle, M., O’Mahony, M.: Google Shared.A Case-Study in Social Search. In: International Conference, UMAP, 17 (2009)
Suri, A., Watts, D.J.: Cooperation and Contagion in Web-Based, Networked Public Goods Experiments. PLoS ONE, 6 e16836 (2011).
Su, X., Khoshgoftaar, T.: A Survey of Collaborative Filtering Techniques. In: Advances in Artificial Intelligence, 17 (2009)
Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 393–408 (1999)
Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inferences. iTrust, Heraklion (2005)
Melville, P., Sindhwani, V.: Recommender Systems. Encyclopedia of Machine Learning, New York (2010)
Vatturi, P.K., Geyer, W., Dugan, C., Muller, B.B.: Tag-based ltering for personalized bookmark recommendations. In: 17th ACM Conference on Information and Knowledge Mining, pp. 1395–1396. ACM, New York (2008)
Ziegler, C., Lausen, G.: Analyzing Correlation Between Trust and User Similarity in Online Communities. In: International Conference on Trust Management, vol.(2) (2004)
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Adabi, A., de Alfaro, L. (2012). Toward a Social Graph Recommendation Algorithm: Do We Trust Our Friends in Movie Recommendations?. In: Herrero, P., Panetto, H., Meersman, R., Dillon, T. (eds) On the Move to Meaningful Internet Systems: OTM 2012 Workshops. OTM 2012. Lecture Notes in Computer Science, vol 7567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33618-8_83
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DOI: https://doi.org/10.1007/978-3-642-33618-8_83
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