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›› 2018, Vol. 33 ›› Issue (4): 668-681.

• Special Section on Recommender Systems with Big Data •

### Multiple Auxiliary Information Based Deep Model for Collaborative Filtering

Lin Yue1,2,3,4, Xiao-Xin Sun1, Wen-Zhu Gao1, Guo-Zhong Feng1,2,*, Bang-Zuo Zhang1,*, Member, CCF, ACM, IEEE

1. 1 School of Information Science and Technology, Northeast Normal University, Changchun 130117, China;
2 Key Laboratory of Applied Statistics of Ministry of Education, Northeast Normal University, Changchun 130024, China;
3 School of Environment, Northeast Normal University, Changchun 130117, China;
4 Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University Changchun 130012, China
• Received:2018-01-14 Revised:2018-05-13 Online:2018-07-05 Published:2018-07-05
• Contact: Guo-Zhong Feng,E-mail:fenggz264@nenu.edu.cn;Bang-Zuo Zhang,E-mail:zhangbz@nenu.edu.cn E-mail:fenggz264@nenu.edu.cn;zhangbz@nenu.edu.cn
• About author:Lin Yue is a postdoctoral research fellow in Northeast Normal University, Changchun. She received her B.S. degree in computer science and technology and M.S. degree in computer application technology from Northeast Normal University, Changchun, in 2009 and 2012, respectively, and her Ph.D. degree in computer application technology from Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, in 2016. She was a joint Ph.D. student in The University of Queensland, Brisbane. Her research interests include data mining, natural language processing and machine learning.
• Supported by:

This work was supported by the National Natural Science Foundation of China under Grant Nos. 71473035 and 11501095, the Fundamental Research Funds for the Central Universities of China under Grant No. 2412017QD028, the China Postdoctoral Science Foundation under Grant No. 2017M621192, the Scientific and Technological Development Program of Jilin Province of China under Grant Nos. 20180520022JH, 20150204040GX, and 20170520051JH, Jilin Province Development and Reform Commission Project of China under Grant Nos. 2015Y055 and 2015Y054, and the Natural Science Foundation of Jilin Province of China under Grant No. 20150101057JC.

With the ever-growing dynamicity, complexity, and volume of information resources, the recommendation technique is proposed and becomes one of the most effective techniques for solving the so-called problem of information overload. Traditional recommendation algorithms, such as collaborative filtering based on the user or item, only measure the degree of similarity between users or items with single criterion, i.e., ratings. According to the experience of previous studies, single criterion cannot accurately measure the similarity between user preferences or items. In recent years, the application of deep learning techniques has gained significant momentum in recommender systems for better understanding of user preferences, item characteristics, and historical interactions. In this work, we integrate plot information as auxiliary information into the denoising autoencoder (DAE), called SemRe-DCF, which aims at learning semantic representations of item descriptions and succeeds in capturing fine-grained semantic regularities by using vector arithmetic to get better rating prediction. The results manifest that the proposed method can effectively improve the accuracy of prediction and solve the cold start problem.

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