A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems  

作　　者：Son-Lam VU Quang-Hung LE 

机构地区：[1]Faculty of Information Technology,Quy Nhon University,Quy Nhon City,Vietnam

出　　处：《Computer Systems Science & Engineering》2023年第1期471-483,共13页计算机系统科学与工程（英文）

基　　金：This work is supported by project No.B2020-DQN-08 from the Ministry of Education and Training of Vietnam.

摘　　要：Recommender systems are similar to an informationfiltering system that helps identify items that best satisfy the users’demands based on their pre-ference profiles.Context-aware recommender systems(CARSs)and multi-criteria recommender systems(MCRSs)are extensions of traditional recommender sys-tems.CARSs have integrated additional contextual information such as time,place,and so on for providing better recommendations.However,the majority of CARSs use ratings as a unique criterion for building communities.Meanwhile,MCRSs utilize user preferences in multiple criteria to better generate recommen-dations.Up to now,how to exploit context in MCRSs is still an open issue.This paper proposes a novel approach,which relies on deep learning for context-aware multi-criteria recommender systems.We apply deep neural network(DNN)mod-els to predict the context-aware multi-criteria ratings and learn the aggregation function.We conduct experiments to evaluate the effect of this approach on the real-world dataset.A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.

关 键 词：Recommender systems CONTEXT-AWARE MULTI-CRITERIA deep learning deep neural network 

分 类 号：TP391.3[自动化与计算机技术—计算机应用技术]