Abstract
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predict users’ possible rankings on all items. For this, we present LwRec, a novel listwise ranking-oriented matrix factorization algorithm. It aims to predict the missing values in the user-ranking probability matrix, aiming that each row of the final predicted matrix should have a probability distribution similar to the original one. Extensive offline experiments on two benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal.
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References
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI, pp. 43–52 (1998)
Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: ICML, pp. 129–136 (2007)
Gopalan, P., Hofman, J.M., Blei, D.M.: Scalable recommendation with hierarchical poisson factorization. In: UAI, pp. 326–335 (2015)
Gunawardana, A., Shani, G.: A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009)
Herlocker, J., Konstan, J.A., Riedl, J.: An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5(4), 287–310 (2002)
Huang, S., Wang, S., Liu, T.Y., Ma, J., Chen, Z., Veijalainen, J.: Listwise collaborative filtering. In: SIGIR, pp. 343–352 (2015)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Kabbur, S., Ning, X., Karypis, G.: FISM: Factored item similarity models for top-N recommender systems. In: SIGKDD, pp. 659–667 (2013)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)
Kullback, S.: Information Theory And Statistics. Dover Pubns, New York (1997)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS, pp. 556–562 (2000)
Lee, J., Kim, S., Lebanon, G., Singer, Y.: Local low-rank matrix approximation. In: ICML, pp. II–82–II–90 (2013)
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)
Liu, N.N., Yang, Q.: EigenRank: a ranking-oriented approach to collaborative filtering. In: SIGIR, pp. 83–90 (2008)
Marden, J.I.: Analyzing and Modeling Rank Data. CRC Press, London (1996)
Ning, X., Karypis, G.: SLIM: sparse linear methods for top-N recommender systems. In: ICDM, pp. 497–506 (2011)
Pan, W., Chen, L.: GBPR: group preference based Bayesian personalized ranking for one-class collaborative filtering. In: IJCAI, pp. 2691–2697 (2013)
Rendle, S.: Factorization Machines with libFM. ACM Trans. Intell. Syst. Technol. 3(3), 57:1–57:22 (2012)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, pp. 1257–1264 (2007)
Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Oliver, N., Hanjalic, A.: CLiMF: learning to maximize reciprocal rank with collaborative less-is-more filtering. In: RecSys, pp. 139–146 (2012)
Shi, Y., Larson, M., Hanjalic, A.: List-wise learning to rank with matrix factorization for collaborative filtering. In: RecSys, pp. 269–272 (2010)
Wang, S., Sun, J., Gao, B.J., Ma, J.: VSRank: a novel framework for ranking-based collaborative filtering. ACM Trans. Intell. Syst. Technol. 5(3), 51:1–51:24 (2014)
Weimer, M., Karatzoglou, A., Le, Q.V., Smola, A.: CofiRank: maximum margin matrix factorization for collaborative ranking. In: NIPS, pp. 1593–1600 (2007)
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Pandey, G., Wang, S. (2019). Listwise Recommendation Approach with Non-negative Matrix Factorization. In: Czarnowski, I., Howlett, R., Jain, L., Vlacic, L. (eds) Intelligent Decision Technologies 2018. KES-IDT 2018 2018. Smart Innovation, Systems and Technologies, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-319-92028-3_3
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DOI: https://doi.org/10.1007/978-3-319-92028-3_3
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