Abstract
In this work we show how items in recommender systems mutually influence each other’s utility and how it can be explored to improve recommendations. The way we model mutual influence is cheap and can be computed without requiring any source of content information about either items or users. We propose an algorithm that considers mutual influence to generate recommendations and analyse it over different recommendation datasets. We compare our algorithm with the Top − N selection algorithm and obtain gains up to 17% in the utility of recommendations without affecting their diversity. We also analyse the scalability of our algorithm and show that it is as applicable for real-world recommender systems as Top − N.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02432-5_33
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References
Toffler, A.: Future Shock. Random House (1970)
Adomavicius, G., Tuzhilin, A.: Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)
Tversky, A.: Elimination by aspects: A theory of choice. Psychological Review 79(4), 281–299 (1972)
Passos, A., Gael, J.V., Herbrich, R., Paquet, U.: A penny for your thoughts? the value of information in recommendation systems. In: NIPS Workshop on Bayesian Optimization, Experimental Design, and Bandits, pp. 9–14 (2011)
Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: RecSys., pp. 109–116 (2011)
Cremonesi, P., Koren, Y., Turrin, R.: Performance of recommender algorithms on top-n recommendation tasks. In: RecSys., pp. 39–46 (2010)
Wang, J.: Mean-variance analysis: A new document ranking theory in information retrieval. In: Boughanem, M., Berrut, C., Mothe, J., Soule-Dupuy, C. (eds.) ECIR 2009. LNCS, vol. 5478, pp. 4–16. Springer, Heidelberg (2009)
Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer (2011)
Xiong, C., Wang, T., Ding, W., Shen, Y., Liu, T.Y.: Relational click prediction for sponsored search. In: WSDM, pp. 493–502 (2012)
Weston, J., Blitzer, J.: Latent structured ranking. In: UAI, pp. 903–913 (2012)
Papagelis, M., Plexousakis, D.: Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Engineering Applications of Artificial Intelligence 18(7), 781–789 (2005)
Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: SIGKDD, pp. 426–434 (2008)
Nemhauser, G., Wolsey, L.: Integer and combinatorial optimization. Wiley (1988)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)
McNee, S.M., Riedl, J., Konstan, J.A.: Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: SIGCHI, pp. 1097–1101 (2006)
Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists. In: RecSys., pp. 123–130 (2008)
Bell, R., Koren, Y.: Lessons from the netflix prize challenge. ACM SIGKDD Explorations Newsletter 9(2) (2007)
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Bessa, A., Veloso, A., Ziviani, N. (2013). Using Mutual Influence to Improve Recommendations. In: Kurland, O., Lewenstein, M., Porat, E. (eds) String Processing and Information Retrieval. SPIRE 2013. Lecture Notes in Computer Science, vol 8214. Springer, Cham. https://doi.org/10.1007/978-3-319-02432-5_6
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DOI: https://doi.org/10.1007/978-3-319-02432-5_6
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