Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Community gravity: measuring bidirectional effects by trust and rating on online social networks
Proceedings of the 18th international conference on World wide web
Rainbow Product Ranking for Upgrading E-Commerce
IEEE Internet Computing
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Large online product review websites (e.g., Epinions, Blippr)to various types of products. Typically, each product in these sites is associated with a group of members who have provided ratings and comments on it. These people form a product community. A potential member can join a produce community by giving a new rating to the product. We refer to this phenomenon of a product community's ability to "attract" new members as product affinity. The knowledge of a ranked list of products based on product affinity is of much importance to be utilized for implementing policies, marketing research, online advertisement, and other applications. In this paper, we identify and analyze an array of features that exert effect on product affinity and propose a novel model, called AffRank, that utilizes these features to predict the future rank of products according to their affinities. Evaluated on a real-world dataset, we demonstrate the effectiveness and superior prediction quality of AffRank compared to baseline methods. Our experiments show that features such as affinity rank history, affinity evolution distance, and average rating are the most important factors affecting future rank of products.