2017/10/23 by Yong Zheng, Zheng, Yong
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Management and Algorithms #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.1710.08516
openalex publication_date 2017/10/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recommender systems have been widely applied to assist user's decision making by providing a list of personalized item recommendations. Context-aware recommender systems (CARS) additionally take context information into considering in the recommendation process, since user's tastes on the items may vary from contexts to contexts. Several context-aware recommendation algorithms have been proposed and developed to improve the quality of recommendations. However, there are limited research which explore and discuss the capability of interpreting the contextual effects by the recommendation models. In this paper, we specifically focus on different contextual modeling approaches, reshape the structure of the models, and exploit how to utilize the existing contextual modeling to interpret the contextual effects in the recommender systems. We compare the explanations of contextual effects, as well as the recommendation performance over two-real world data sets in order to examine the quality of interpretations.