Contextualized Recommendation Model Based Socio-Environmental Factors
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REMOVE: REcommendation Model based on sOcio-enVironmental contExt
AbstractRecommender Systems (RS) suffer from the typical new user and data sparsity problems. In order to reduce these issues, a RecommEndation Model based on sOcio-enVironmental contExt called REMOVE is proposed in this paper. By elaborating the state-of-...
Typicality-Based Collaborative Filtering Recommendation
Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas ...
Attention-driven Factor Model for Explainable Personalized Recommendation
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalLatent Factor Models (LFMs) based on Collaborative Filtering (CF) have been widely applied in many recommendation systems, due to their good performance of prediction accuracy. In addition to users' ratings, auxiliary information such as item features ...
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New York, NY, United States
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