›› 2018,Vol. 33 ›› Issue (4): 668-681.doi: 10.1007/s11390-018-1848-x

所属专题: 3 Artificial Intelligence and Pattern Recognition Data Management and Data Mining

• Special Section on Computer Networks and Distributed Computing • 上一篇    下一篇

基于多重辅助信息及深度模型的协同过滤

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
  • 收稿日期:2018-01-14 修回日期:2018-05-13 出版日期:2018-07-05 发布日期:2018-07-05
  • 通讯作者: 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
  • 作者简介: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.
  • 基金资助:

    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.

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.

随着信息资源的动态性、复杂性和信息量的不断增加,推荐技术成为解决信息过载问题的最有效的技术之一。传统的推荐算法,如基于用户或物品的协同过滤,利用评分信息衡量用户或物品之间的相似程度。然而单一评分不能准确衡量用户偏好或物品之间的相似度,从而准确推荐。近年来,深度学习技术的应用在推荐系统中获得了显著的发展势头,用以更好地理解用户偏好、物品特征和历史交互等。在本文工作中,我们在降噪自编码器中融合影评信息作为辅助信息,即SemRe-DCF,本方法旨在学习物品描述的语义表示,及利用向量算法捕获细粒度的语义规则,从而获得更好的评分预测。结果表明,该方法能有效地提高预测精度和冷启动问题。

Abstract: 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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