Evidence-driven dubious decision making in online shopping

Nowadays, Online shopping has been tremendous lifestyle choices due to the lower management cost for product/service providers and the cheaper prices for buyers/customers. Meanwhile, it raises a big challenge for both buyers and sellers to identify the right product items from the numerous choices and the right customers from a large number of different buyers. This motivates the study of recommendation system which computes recommendation scores for product items and filters out those with low scores. Recently, a promising direction involves the consideration of the social network influence in recommendation system. While significant performance improvement has been observed, it is still unclear to which extension the social network influence can help differentiate product items in terms of recommendation scores. This is an interesting problem in particular in the situation that the recommended product items have the highly similar (or identical) scores. As the first effort to this problem, this paper probes the boundary of social network influence to recommendation outputs by solving an optimization problem called evidence-driven dubious decision making. Two solutions have been proposed and the evaluation on two real world datasets has verified the effectiveness of the proposed solutions.