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Summary: AffRank: Affinity-Driven Ranking of Products in Online Social
Rating Networks
Hui Li, Sourav S. Bhowmick, Aixin Sun
Nanyang Technological University, Nanyang Avenue, Singapore 639798
herolee@pmail.ntu.edu.sg, {assourav,axsun}@ntu.edu.sg
Abstract
Large online social rating networks (e.g., Epinions, Blippr)
have recently come into being containing information related
to various types of products. Typically, each product in these
networks is associated with a group of members who have pro-
vided ratings and comments on it. These people form a product
community. A potential member can join a product commu-
nity 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 iden-
tify and analyze an array of features that exert effect on product
affinity and propose a novel model, called AffRank, that utilizes
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