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Fusing Similarity Models with Markov Chains for Sparse ...

Fusing Similarity Models with Markov Chains for Sparse sequential Recommendation Ruining He, Julian McAuley Department of Computer Science and Engineering University of California, San Diego Email: {r4he, Similar Abstract Predicting personalized sequential behavior is a key task for recommender systems. In order to predict user actions such as the next product to purchase, movie to watch, predict or place to visit, it is essential to take into account both long- term user preferences and sequential patterns ( , short-term ? dynamics). Matrix Factorization and Markov chain methods action sequence of a certain user have emerged as two separate but powerful paradigms for sequential modeling the two respectively. Combining these ideas has led to unified methods that accommodate long- and short-term Figure 1: An example of how our method, Fossil, makes dynamics simultaneously by modeling pairwise user-item and recommendations .}

Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation Ruining He, Julian McAuley Department of Computer Science and Engineering

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