Using Social Tags and User Rating Patterns for Collaborative Filtering

Abstract

The overwhelming supply of online information on the Web makes finding better ways to separate important information from the noisy data ever more important. Recommender systems may help users deal with the information overloading issue, yet their performance appears to have stalled in currently available approaches. In this study, the authors propose and examine a novel user profiling approach that uses collaborative tagging information to enhance recommendation performance. They evaluate the proposed hybrid approach, illustrated in the context of movie recommendation. The authors also empirically evaluate various existing recommendation approaches (in comparison with the newly proposed approach) using sensitivity analyses to investigate the potential use of varied user rating or tagging patterns to improve recommendations accuracy. The results don't just indicate the effective and competitive performance of the suggested approach, but they also suggest important implications and directions for further research, including the potential associated with applying multiple recommendation approaches within a single system based on the different rating or tagging patterns of the user.

[Retracted] Optimization of the Hybrid Movie Recommendation System Based on Weighted Classification and User Collaborative Filtering Algorithm

“…Bobadilla et al proposed the multidimensional matrix factorization model, combined with collaborative filtering (CF) algorithm, and used user and project attributes to make score prediction, which improved the accuracy of prediction score [12]. e first mock exam algorithm usually constructs a single global model for all users [13][14][15]. It is considered that the similarity of two identical items in any group is the same.…”