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      Local Optimality of User Choices and Collaborative Competitive Filtering

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          Abstract

          While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions but totally disregard the contexts in which users' decisions are made. In this paper, we propose Collaborative Competitive Filtering (CCF), a framework for learning user preferences by modeling the choice process in recommender systems. CCF employs a multiplicative latent factor model to characterize the dyadic utility function. But unlike CF, CCF models the user behavior of choices by encoding a local competition effect. In this way, CCF allows us to leverage dyadic data that was previously lumped together with missing data in existing CF models. We present two formulations and an efficient large scale optimization algorithm. Experiments on three real-world recommendation data sets demonstrate that CCF significantly outperforms standard CF approaches in both offline and online evaluations.

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          Most cited references 14

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          A Logit Model of Brand Choice Calibrated on Scanner Data

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            Factorization meets the neighborhood

             Yehuda Koren (2008)
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              Bayesian probabilistic matrix factorization using Markov chain Monte Carlo

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                Author and article information

                Journal
                04 October 2010
                2011-02-25
                Article
                1010.0621

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                27 pages, 4 figure
                stat.ML cs.IR cs.SI stat.AP

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