An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets.

ABSTRACT: Collaborative filtering (CF) approaches generate user recommendations based on user similarities. These similarities are calculated based on the overall (explicit) user ratings. However, in some domains, such ratings may be sparse or unavailable. User reviews can play a significant role in such cases, as implicit ratings can be derived from the reviews using sentiment analysis, a natural language processing technique. However, most current studies calculate the implicit ratings by simply aggregating the scores of all sentiment words appearing in reviews and, thus, ignoring the elements of sentiment degrees and aspects of user reviews. This study addresses this issue by calculating the implicit rating differently, leveraging the rich information in user reviews by using both sentiment words

SUBMITTER: Al-Ghuribi S 

PROVIDER: S-EPMC10495999 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature