A Survey of Context-Aware Recommender Systems: From an Evaluation Perspective

Xiangwu Meng, Yulu Du, Yujie Zhang, Xiaofeng Han

Abstract: In recent years, context-aware recommender systems (CARSs), which incorporate contextual information to achieve better recommendations, become a hot topic in the domain of recommender systems. Many context-aware recommendation methods have been proposed in the past decades. Some literatures provide survey of the research on CARSs. However, they mainly focus on context-aware recommendation methods and overlook the evaluation of them. The evaluation methods, evaluation properties and datasets of CARSs are different from those of traditional recommender systems where contexts are not considered. In this paper, we provide a review for evaluation of CARSs. We will introduce the basic concepts of CARSs, propose a new dataset partition method for each category of CARSs according to our classification of CARSs, summarize the evaluation method. Then we summarize the evaluation properties that CARSs pays attention to, which are different from the NCARSs. In addition, we also summarize the datasets specifically for CARSs, its applicable CARSs type and its evaluation dimensions and metrics. Based on our review, we draw some conclusions from evaluation perspective and point out future research directions.