FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction
Zhan Su,
Haochuan Yang,
Jun Ai
Abstract:Rating prediction is crucial in recommender systems as it enables personalized recommendations based on different models and techniques, making it of significant theoretical importance and practical value. However, presenting these recommendations in the form of lists raises the challenge of improving the list’s quality, making it a prominent research topic. This study focuses on enhancing the ranking quality of recommended items in user lists while ensuring interpretability. It introduces fuzzy membership fun… Show more
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