SIFN
Peiyan Zhang, Hao Qian, Qi Liu, Zhiqiang Zhang, Jun Zhou, Jianhui Ma, Enhong Chen
Abstract
Recent studies in recommender systems have managed to achieve significantly improved performance. However, despite being extensively studied, these methods still suffer from two limitations. First, previous studies either encode the document or extract latent sentiment via neural networks, which are difficult to interpret the sentiment of reviewers intuitively. Second, they neglect the personalized interaction of reviews with user/item, i.e., each review has different contributions when modeling the preference of user/item
Topics & Concepts
Computer scienceRecommender systemSentiment analysisPreferenceENCODEArtificial intelligenceInformation retrievalMicroeconomicsEconomicsGeneChemistryBiochemistryRecommender Systems and TechniquesTopic ModelingSentiment Analysis and Opinion Mining