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：6
页码：7740-7744
出版社：TechScience Publications
摘要：In this paper, we utilize the We propose a way to make use of profiles to extend the co-rating relation, and then we propose a set of effective features to reflect users’ preferences or items’ reputations in multiple phases of interest, and after that we propose an adaptive algorithm for effective demonstrate endorsement recommendation. In Webbased services of effective content (such as news articles), recommender systems face the difficulty of timely identifying new items of high-quality and providing recommendations for new users. Recommendation techniques are very important in the fields of E-commerce and other Web-based services. One of the main difficulties is dynamically providing high-quality recommendation on scarce data . In this paper, a novel effective demonstrate recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of effective features are designed to describe user preferences in multiple phases, and finally a recommendation is made by adaptively weighting the features. Experimental results on public datasets show that the proposed algorithm has satisfying performance. We propose a feature based machine learning approach.
关键词：Web-based services; collaborative filtering; Hybrid;approaches; Flexibility ; Light computation; personalization;cold-start problem; summarizing; flexibility; information to;deal with the effective nature.
