›› 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.

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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.

[1] Wang J, de Vries A P, Reinders M J. Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In Proc. the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2006, pp.501-508.

[2] Jiang S, Qian X, Shen J, Fu Y, Mei T. Author topic modelbased collaborative filtering for personalized POI recommendations. IEEE Transactions on Multimedia, 2015, 17(6):907-918.

[3] Yang B, Lei Y, Liu J, Li W. Social collaborative filtering by trust. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8):1633-1647.

[4] Song Y, Elkahky A M, He X. Multi-rate deep learning for temporal recommendation. In Proc. the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, July 2016, pp.909-912.

[5] Rawat Y S, Kankanhalli M S. ConTagNet:Exploiting user context for image tag recommendation. In Proc. the 2016 ACM on Multimedia Conference, October 2016, pp.1102-1106.

[6] Wang H, Xingjian S H I, Yeung D Y. Collaborative recurrent autoencoder:Recommend while learning to fill in the blanks. In Advances in Neural Information Processing Systems, Lee D D, Sugiyama M, Luxburg U V et al. (eds.), Neural Information Processing Systems Foundation, Inc., 2016, pp.415-423.

[7] Zhang F, Yuan N J, Lian D, Xie X, Ma W Y. Collaborative knowledge base embedding for recommender systems. In Proc. the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2016, pp.353-362.

[8] Huang W, Wu Z, Chen L, Mitra P, Giles C L. A neural probabilistic model for context based citation recommendation. In Proc. AAAI, January 2015, pp.2404-2410.

[9] Pana Y, Hea F, Yua H. Trust-aware collaborative denoising auto-encoder for top-n recommendation. arXiv:1703.01760, 2017. https://arxiv.org/abs/1703.01760, May 2018.

[10] Wang X, Yu L, Ren K, Tao G, Zhang W, Yu Y, Wang J. Dynamic attention deep model for article recommendation by learning human editors' demonstration. In Proc. the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2017, pp.2051-2059.

[11] Wu C, Wang J, Liu J, Liu W. Recurrent neural network based recommendation for time heterogeneous feedback. Knowledge-Based Systems, 2016, 109:90-103.

[12] Elkahky A M, Song Y, He X. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.278-288.

[13] Jia X, Li X, Li K, Gopalakrishnan V, Xun G, Zhang A. Collaborative restricted Boltzmann machine for social event recommendation. In Proc. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), August 2016, pp.402-405.

[14] Zheng Y, Tang B, Ding W, Zhou H. A neural autoregressive approach to collaborative filtering. arXiv:1605.09477, 2016. https://arxiv.org/pdf/1605.09477, May 2018.

[15] Zhang S, Yao L, Sun A. Deep learning based recommender system:A survey and new perspectives. arXiv:1707.07435, 2017. https://arxiv.org/abs/1707.07435, May 2018.

[16] Yin H, Zhou X, Cui B, Wang H, Zheng K, Nguyen Q V H. Adapting to user interest drift for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(10):2566-2581.

[17] Yin H, Wang W, Wang H, Chen L, Zhou X. Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(11):2537-2551.

[18] Yin H, Cui B. Spatio-Temporal Recommendation in Social Media. Springer Singapore, 2016.

[19] Yin H, Hu Z, Zhou X, Wang H, Zheng K, Nguyen Q V H, Sadiq S. Discovering interpretable geo-social communities for user behavior prediction. In Proc. the 32nd International Conference on Data Engineering (ICDE), May 2016, pp.942-953.

[20] Xie M, Yin H, Wang H, Xu F, Chen W, Wang S. Learning graph-based POI embedding for location-based recommendation. In Proc. the 25th ACM International Conference on Information and Knowledge Management, October 2016, pp.15-24.

[21] Yin H, Cui B, Zhou X, Wang W, Huang Z, Sadiq S. Joint modeling of user check-in behaviors for real-time point-ofinterest recommendation. ACM Transactions on Information Systems (TOIS), 2016, 35(2):Article No. 11.

[22] Tong Y, Chen L, Zhou Z, Jagadish H V, Shou L, Lv W. SLADE:A smart large-scale task decomposer in crowdsourcing. IEEE Transactions on Knowledge and Data Engineering, 2018. DOI:10.1109/TKDE.2018.2797962.

[23] Tong Y, She J, Ding B, Wang L, Chen L. Online mobile micro-task allocation in spatial crowdsourcing. In Proc. the 32nd International Conference on Data Engineering, May 2016, pp.49-60.

[24] Tong Y, She J, Ding B, Chen L, Wo T, Xu K. Online minimum matching in real-time spatial data:Experiments and analysis. Proc. the VLDB Endowment, 2016, 9(12):1053-1064.

[25] Sedhain S, Menon A K, Sanner S, Xie L. AutoRec:Autoencoders meet collaborative filtering. In Proc. the 24th International Conference on World Wide Web, May 2015, pp.111-112.

[26] Strub F, Gaudel R, Mary J. Hybrid recommender system based on autoencoders. In Proc. the 1st Workshop on Deep Learning for Recommender Systems, September 2016, pp.11-16.

[27] Strub F, Mary J. Collaborative filtering with stacked denoising autoencoders and sparse inputs. In Proc. NIPS Workshop on Machine Learning for eCommerce, December 2015.

[28] Ouyang Y, Liu W, Rong W, Xiong Z. Autoencoder-based collaborative filtering. In Proc. International Conference on Neural Information Processing, November 2014, pp.284-291.

[29] Wu Y, DuBois C, Zheng A X, Ester M. Collaborative denoising auto-encoders for top-n recommender systems. In Proc. the 9th ACM International Conference on Web Search and Data Mining, February 2016, pp.153-162.

[30] Wang H, Wang N, Yeung D Y. Collaborative deep learning for recommender systems. In Proc. the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2015, pp.1235-1244.

[31] Ying H, Chen L, Xiong Y, Wu J. Collaborative deep ranking:A hybrid pair-wise recommendation algorithm with implicit feedback. In Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, April 2016, pp.555-567.

[32] Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR:Bayesian personalized ranking from implicit feedback. In Proc. the 25th Conference on Uncertainty in Artificial Intelligence, June 2009, pp.452-461.

[33] Li S, Kawale J, Fu Y. Deep collaborative filtering via marginalized denoising auto-encoder. In Proc. the 24th ACM International Conference on Information and Knowledge Management, October 2015, pp.811-820.

[34] Zhang S, Yao L, Xu X. AutoSVD++:An efficient hybrid collaborative filtering model via contractive auto-encoders. arXiv:1704.00551, 2017. https://arxiv.org/pdf/1704.00551, May 2018.

[35] Wei J, He J, Chen K, Zhou Y, Tang Z. Collaborative filtering and deep learning based hybrid recommendation for cold start problem. In Proc. the 14th Int. Conf. Pervasive Intelligence and Computing and the 2nd Int. Conf. Big Data Intelligence and Computing and Cyber Science and Technology Congress, August 2016, pp.874-877.

[36] Wei J, He J, Chen K, Zhou Y, Tang Z. Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 2017, 69:29-39.

[37] Strub F, Mary J, Gaudel R. Hybrid collaborative filtering with neural networks. https://hal.archives-ouvertes.fr/hal-01281794v1/document, June 2018.

[38] Pennington J, Socher R, Manning C. Glove:Global vectors for word representation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 2014, pp.1532-1543.

[39] Zhou Y, Wilkinson D, Schreiber R, Pan R. Large-scale parallel collaborative filtering for the Netflix prize. In Proc. International Conference on Algorithmic Applications in Management, June 2008, pp.337-348.

[40] Li S, Kawale J, Fu Y. Deep collaborative filtering via marginalized denoising auto-encoder. In Proc. the 24th ACM International Conference on Information and Knowledge Management, October 2015, pp.811-820.

[41] LeCun Y, Bottou L, Orr G B, Müller K R. Efficient backprop. Neural Networks:Tricks of the Trade, 1998, 1998:9-50.
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[1] 高庆狮; 张祥; 杨树范; 陈树清;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[2] 闵应骅;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[3] 段平; 蔡希尧;. A Real-Time Interprocessor Synchronization Algorithm for Communication in Distributed Computer Systems[J]. , 1987, 2(4): 292 -302 .
[4] 金泓平; 顾君忠;. The Optimization of Distributed Join in C-POREL System[J]. , 1987, 2(4): 276 -286 .
[5] 史维更;. Detecting and Locating Failures in Communication Networks[J]. , 1990, 5(3): 275 -288 .
[6] 金志权; 柳诚飞; 孙钟秀; 周晓方; 陈佩佩; 顾建明;. Design and Implementation of a Heterogeneous Distributed Database System[J]. , 1990, 5(4): 363 -373 .
[7] 周权; 魏道政;. A Complete Critical Path Algorithm for Test Generation of Combinational Circuits[J]. , 1991, 6(1): 74 -82 .
[8] 郭恒昌;. On the Characterization and Fault Identification of Sequentially t-Diagnosable System Under PMC Model[J]. , 1991, 6(1): 83 -90 .
[9] 周启海;. An Improved Graphic Representation for Structured Program Design[J]. , 1991, 6(2): 205 -208 .
[10] 史维更; Brigitte Servatius;. Improving the Reliability of Computer Communication Networks[J]. , 1991, 6(3): 230 -242 .
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