2018/09/09 by Mohammad Reza Zarei, Zarei, M. R., Mohammad R. Moosavi +1 · 1 citation
Computer Science · #Advanced Data Compression Techniques #FOS: Computer and information sciences #Image and Video Quality Assessment #Recommender Systems and Techniques #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1809.03047
openalex publication_date 2018/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recommender systems play a significant role in providing the appropriate data for each user among a huge amount of information. One of the important roles of a recommender system is to predict the preference of each user to some specific data. Some of these systems concentrate on user-item networks that each user rates some items. The main step for item recommendation is to predict the rate of unrated items. Each recommender system utilizes different criteria such as the similarity between users or social relations in the process of rate prediction. As social connections of each user affect his behaviors, it can be a valuable source to use in rate prediction. In this paper, we will provide a new social recommender system which uses Bhattacharyya coefficient in similarity computing to be able to evaluate similarity in sparse data and between users without co-rated items as well as integrating social ties into the rating prediction process.