Representation learning: serial-autoencoder for personalized recommendation  

作　　者：Yi ZHU Yishuai GENG Yun LI Jipeng QIANG Xindong WU 

机构地区：[1]School of Information Engineering,Yangzhou University,Yangzhou 225127,China [2]Key Laboratory of Knowledge Engineering with Big Data(Ministry of Education of the People’s Republic of China),Hefei University of Technology,Hefei 230009,China [3]School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China

出　　处：《Frontiers of Computer Science》2024年第4期61-72,共12页中国计算机科学前沿（英文版）

基　　金：National Natural Science Foundation of China(Grant Nos.61906060,62076217,and 62120106008);National Key R&D Program of China(No.2016YFC0801406);Natural Science Foundation of the Jiangsu Higher Education Institutions(No.20KJB520007).

摘　　要：Nowadays,the personalized recommendation has become a research hotspot for addressing information overload.Despite this,generating effective recommendations from sparse data remains a challenge.Recently,auxiliary information has been widely used to address data sparsity,but most models using auxiliary information are linear and have limited expressiveness.Due to the advantages of feature extraction and no-label requirements,autoencoder-based methods have become quite popular.However,most existing autoencoder-based methods discard the reconstruction of auxiliary information,which poses huge challenges for better representation learning and model scalability.To address these problems,we propose Serial-Autoencoder for Personalized Recommendation(SAPR),which aims to reduce the loss of critical information and enhance the learning of feature representations.Specifically,we first combine the original rating matrix and item attribute features and feed them into the first autoencoder for generating a higher-level representation of the input.Second,we use a second autoencoder to enhance the reconstruction of the data representation of the prediciton rating matrix.The output rating information is used for recommendation prediction.Extensive experiments on the MovieTweetings and MovieLens datasets have verified the effectiveness of SAPR compared to state-of-the-art models.

关 键 词：personalized recommendation autoencoder representation learning collaborative filtering 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]