A Social Recommender System based on Bhattacharyya Coefficient

TL;DR

This paper introduces a social recommender system that employs the Bhattacharyya coefficient to improve similarity measurement, especially in sparse data scenarios and without requiring co-rated items, integrating social ties into predictions.

Contribution

It presents a novel social recommender system utilizing the Bhattacharyya coefficient for enhanced similarity evaluation in sparse and co-rated data scenarios.

Findings

Improved accuracy in sparse data conditions

Effective use of social ties in rating prediction

Enhanced similarity measurement without co-rated items

Abstract

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.