期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2014
卷号：5
期号：5
页码：6652-6655
出版社：TechScience Publications
摘要：The Internet gives users easy and immediate access to a lot of resources (documents, media, services, etc.). Among this abundance of items, information overload is an evergrowing problem. Recommender systems are one classically proposed solution to cope with this problem. We propose an evolution of trust-based recommender systems that only relies on local information and can be deployed on top of existing social networks. Our approach takes into account friends' similarity and confidence on ratings, but limits data exchange to direct friends, in order to prevent ratings from being globally known. Therefore, calculations are limited to locally processed algorithms, privacy concerns can be taken into account and algorithms are suitable for decentralized or peer-to-peer architectures. We show that local information with good default scoring strategies is sufficient to cover more users than classical collaborative filtering and trust-based recommender systems. Regarding accuracy, our approach performs better than most others, especially for cold start users, despite using less information. To propose replicas further complex to current records of transactions by accumulation a disremembering factor into the N-HMM based methods and utilize other optimization algorithms to diminish the effect of N-HMM’s initialization on performance.
