Abstract
Diversifying the recommendation lists in recommendation systems could potentially satisfy user’s needs. Most diversification techniques are designed to recommend the top-k relevant and diverse items, which take the coverage of the user preferences into account. The relevance scores are usually estimated by methods such as latent matrix factorization. While in this paper, we model the users’ interests with the topic distributions on the rated items. And then we investigate how to improve the topic diversification within the recommendation lists. We first estimate the topic distributions of users and items through training Latent Dirichlet Allocation (LDA) on the rating set. After that we propose two topic diversification methods based on submodular function maximization and proportionality respectively. Experimental results on MovieLens and FilmTrust datasets demonstrate that our approach outperforms state-of-the-art techniques in terms of distributional diversity.
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Zhang, M., Hurley, N.: Avoiding monotony: improving the diversity of recommendation lists, Lausanne, Switzerland, 23–25 October 2008 (2008)
Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions - I. Math. Program. 14, 265–294 (1978)
Agrawal, R., Gollapudi, S., Halverson, A., Ieong, S.: Diversifying search results. In: WSDM 2009, pp. 5–14 (2009)
Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: SIGIR 1998, Melbourne, Australia (1998)
Kim, Y., Shim, K.: TWILITE: a recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation. Inf. Syst. 42, 59–77 (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J.R., Zhang, Y.-C.: Solving the apparent diversity-accuracy dilemma of recommender systems. In: PNAS, vol. 107 (2010)
Ziegler, C., McNee, S., Konstan, J., Lausen, G.: Improving recommendation lists through topic diversification. In: WWW, pp. 22–32 (2005)
Ashkan, A., Kveton, B., Berkovsky, S., Wen, Z.: Optimal greedy diversity for recommendation. In: IJCAI 2015, pp. 1742–1748 (2015)
Di Noia, T., Ostuni, V.C., Rosati, J., Tomeo, P., Di Sciascio, E.: An analysis of users’ propensity toward diversity in recommendations. In: RecSys 2014 (2014)
Wu, L., Liu, Q., Chen, E., Yuan, N.J., Guo, G., Xie, X.: Relevance meets coverage: a unified framework to generate diversified recommendations. ACM TIST 7, 39 (2016)
Ashkan, A., Kveton, B., Berkovsky, S., Wen, Z.: Diversified utility maximization for recommendations. In: RecSys Poster Proceedings (2014)
Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook, pp. 145–186 (2011)
Van Dang, W., Croft, B.: Diversity by proportionality: an election-based approach to search result diversification. In: SIGIR 2012, Portland, OR, USA, 12–16 August 2012 (2012)
Sha, C., Wu, X., Niu, J.: A framework for recommending relevant and diverse items. In: IJCAI 2016, New York, NY, USA, 9–15 July 2016 (2016)
Qin, L., Zhu, X.: Promoting diversity in recommendation by entropy regularizer. In: IJCAI, pp. 2698–2704 (2013)
Vargas, S., Castells, P., Vallet, D.: Explicit relevance models in intent-oriented information retrieval diversification. In: SIGIR 2012, Portland, OR, USA, 12–16 August 2012 (2012)
Santos, R.L., Macdonald, C., Ounis, I.: Exploiting query reformulations for web search result diversification. In: WWW 2010 (2010)
Barbieri, N., Manco, G.: An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering. ECML PKDD 2011
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Xing, X., Sha, C., Niu, J. (2017). Improving Topic Diversity in Recommendation Lists: Marginally or Proportionally?. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_12
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DOI: https://doi.org/10.1007/978-3-319-63564-4_12
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