Recommendation system in social networks with topical attention and probabilistic matrix factorization.

ABSTRACT: Collaborative filtering (CF) is a common recommendation mechanism that relies on user-item ratings. However, the intrinsic sparsity of user-item rating data can be problematic in many domains and settings, limiting the ability to generate accurate predictions and effective recommendations. At present, most algorithms use two-valued trust relationship of social network to improve recommendation quality but fail to take into account the difference of trust intensity of each friend and user's comment information. To this end, the recommendation system within a social network adopts topical attention and probabilistic matrix factorization (STAPMF) is proposed. We combine the trust information in social networks and the topical information from review documents by proposing a novel algorithm co

SUBMITTER: Zhang W 

PROVIDER: S-EPMC6822766 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature