Trust and Distrust based Cross-domain Recommender System

ABSTRACT

A recommender system (RS) provides assistance for users to filter out items of their interest in the presence of millions of available items. The reason is to find out the likewise user with the assumption that if users have shared similar interest in the past then they may share the same in future. Collaborative filtering (CF) is the widely used recommendation algorithm due to its ease of use but suffers with the problems of sparsity and cold start problem. In this paper, we propose a trust and distrust-based cross domain context aware recommender system in the multi-agent environment which tries to reduce the problem of data sparsity in collaborative-filtering recommender system and improves coverage. Cross Domain Recommender System (CDRS) utilizes data from multiple domains to reduce the problem of sparsity. Moreover, the combination of trust and distrust in recommendation help to improve trustworthiness of generated recommendation. Distrust provides higher accuracy in recommendation by incorporating knowledge about the malicious users. Prototype of the system is developed using JADE and Java technology for the tourism domain consisting of restaurant, hotel, travel places and shopping places as sub-domains. The performance of the proposed trust and distrust-based cross domain recommender system is compared with the traditional approach of recommendation along with the cross domain approach and trust-based cross-domain approach in terms of accuracy and coverage. The results show that the proposed system outperforms in terms of both accuracy and coverage.

KEYWORDS:

• Recommender Systems
• Collaborative Filtering
• Trust; Distrust
• Cross-domain
• Context-aware
• Multiagent Systems

Acknowledgments

The Author duly acknowledges University Grant Commission (UGC) of India for supporting this research work via UGC MRP Grant No. [42-139/2013 (SR)] to Dr. Punam Bedi.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.