期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：1
DOI：10.14569/IJACSA.2016.070161
出版社：Science and Information Society (SAI)
摘要：Recommender systems suggest a list of interesting items to users based on their prior purchase or browsing behaviour on e-commerce platforms. The continuing research in recommender systems have primarily focused on developing algorithms for rating prediction task. However, most e-commerce platforms provide ‘top-k’ list of interesting items for every user. In line with this idea, the paper proposes a novel machine learning algorithm to predict a list of ‘top-k’ items by optimizing the latent factors of users and items with the mapped scores from ratings. The basic idea is to learn latent factors based on the cosine similarity between the users and items latent features which is then used to predict the scores for unseen items for every user. Comprehensive empirical evaluations on publicly available benchmark datasets reveal that the proposed model outperforms the state-of-the-art algorithms in recommending good items to a user.
