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Incorporating phrase-level sentiment analysis on users' textual reviews for recommendation has became a popular method due to its explainable property for latent features and high prediction accuracy. However, the inherent limitations of the existing model make it difficult to (1) effectively distinguish the features that are most interesting to users, (2) maintain the recommendation performance especially when the set of items is scaled up to multiple categories, and (3) model users' implicitdoi:10.1145/2911451.2911549 dblp:conf/sigir/ChenQZX16 fatcat:vwurg5jed5eixbjxllanoqhzj4