FPLV: Enhancing recommender systems with fuzzy preference, vector similarity, and user community for rating prediction.

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 functions to measure user attributes on a multi-dimensional item label vector and calculates user similarity based on these features for prediction and recommendation. Additionally, the user similarity network is modeled to extract community information, leading to the design of a set of

SUBMITTER: Su Z 

PROVIDER: S-EPMC10461840 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature