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This paper presents a model to enhance collaborative filtering recommender systems by incorporating user reviews alongside ratings to improve user similarity estimation. The authors explore seven different approaches to compute text similarity of user reviews, demonstrating that their model significantly improves recommender system performance, particularly with the Long Short Term Memory (LSTM) network. The study highlights the importance of leveraging user-generated content to refine recommendations in th

Author

sreenivasa sreenu

Language

EN