Authors: Sanjeev Kumar Sharma; Ugrasen Suman
Addresses: School of Computer Science and Information Technology, Devi Ahilya University, Takshashilla Campus, Khandwa Road, Indore, 452001, Madhya Pradesh, India ' School of Computer Science and Information Technology, Devi Ahilya University, Takshashilla Campus, Khandwa Road, Indore, 452001, Madhya Pradesh, India
Abstract: Recommender systems have been used to suggest the interesting items such as movies, books and songs according to the choice of users. These systems compute a user similarity among users and use it as a weight for the users' ratings. However, they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. The traditional recommender system techniques are often ineffective and are not able to compute a user similarity weight for many of the users. The trust among two or more users in the web of trust increases the quality of recommendation in two ways. Firstly, the trust metrics reduce the computability of similarity assessment of users or items. Secondly, the reputation of users may be computed using trust propagation. In this paper, architecture of trust-based recommender systems is proposed. In which trust metrics and rating matrix are taken as input and neighbours are generated using trust metrics and user similarity respectively and importance of trust over collaborative filtering is described. In the proposed approach, trust-based issues are discussed to solve the problem of traditional recommender system such as, data sparsity, cold-start users, malicious attacks on recommender systems and centralised architectures.
Keywords: trust; user similarity; recommender systems; collaborative filtering; semantic enhanced personaliser; SEP; personalisation; recommendations; data sparsity; cold start users; malicious attacks; centralised architectures.
International Journal of Business Information Systems, 2014 Vol.16 No.2, pp.134 - 153
Published online: 25 Jul 2014 *Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article