Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation
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, or place to visit, it is essential to take into account both long-term user preferences and sequential patterns (i.e., short-term dynamics). Matrix Factorization …