Year: 2020
Type: article
Abstract: User interests modeling has been exploited as a critical component to improve the predictive performance of recommender systems. However, with the absence of explicit information to model user interests, most approaches to recommender systems exploit users activities (user generated contents or user ratings) to inference the interest of users. In reality, the relationship among users also serves as a rich source of information of shared interest. To this end, we propose a framework which avoids the sole dependence of user activities to infer user interests and allows the exploitation of the direct relationship between users to propagate user interests to improve system's performance. In this paper, we advocate a novel modeling framework. We construct a probabilistic user interests model and propose a user interests propagation algorithm (UIP), which applies a factor graph based approach to estimate the distribution of the interests of users. Moreover, we incorporate our UIP algorithm with conventional matrix factorization (MF) for recommender systems. Experimental results demonstrate that our proposed approach outperforms previous methods used for recommender systems.
Source: IEEE Access
Authors: Samuel Mensah, Chunming Hu, Xue Li, Xudong Liu, Richong Zhang
Institution: Beihang University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,750
Awards: 基于多源异构数据的知识图谱构建、推理与问答研究, G5795226039
OpenAlex ID: W3035285349