Item Reputation-Aware Recommender Systems

Abstract

Recommender systems provide personalized advice for online customers based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems employ users' ratings to generate their output. In this paper, we aim to combine reputation models with recommender systems to enhance the accuracy of recommendations. Our proposed methods make two contributions. First of all, we propose two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively. In addition, a novel personalized reputation method is designed in order to generate item reputations based upon users' interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.

State of the art of reputation-enhanced recommender systems

“…The item with the highest total score is recommended to the user. Abdel-Hafez et al [2] introduce a recursive variant of this approach. In another proposal belonging to this category, Wang et al [53] suggest the weighted enhancement of a product's recommendation value with its reputation and its purchase frequency.…”

Reputation-Enhanced Recommender Systems

“…The item with the highest total score is recommended to the user. Abdel-Hafez et al [2] introduce a recursive variant of this approach. In another proposal belonging to this category, Wang et al [44] suggest the weighted enhancement of a product's recommendation value with its reputation and its purchase frequency.…”