guo wu - Key Laboratory of Broadband Network and Multimedia, Graduate School at Shenzhen, Tsinghua University Shenzhen, Guangdong Province, China
yujiu yang - Key Laboratory of Broadband Network and Multimedia, Graduate School at Shenzhen, Tsinghua University Shenzhen, Guangdong Province, China
Wenhuang liu - Key Laboratory of Broadband Network and Multimedia, Graduate School at Shenzhen, Tsinghua University Shenzhen, Guangdong Province, China

Traditional neighborhood-based collaborative filtering algorithms are widely used in recommender system field for its accuracy, interpretability and operability. In this paper, weintroduce expert user model into collaborative filtering and determine authoritative expert users via expert finding methodsin large corpus. We propose a method to produce predications for target user. Instead of the similarity between normal users and target user, we determine target user’s neighborhood basedon the similarity be-tween expert users and target user. Experiments on Amazon datasets show that our method hasbetter performance than neighbor-hood-based collaborative filtering on recommendation accuracy, novelty and calculationefficiency.

Domain, Expert, Recommender System, Collaborative Filtering