Computer Science ›› 2015, Vol. 42 ›› Issue (11): 96-100.doi: 10.11896/j.issn.1002-137X.2015.11.020

Previous Articles     Next Articles

Neural-network-based Method for Automatic Acquisition of User’s Video Rating

JI Shu-juan, WANG Li, LIANG Yong-quan and ZHAO Jian-li   

  • Online:2018-11-14 Published:2018-11-14

Abstract: In future intelligent system,a real intelligent video recommendation system should have the ability of automatically acquiring users’ interest and preference without users’ rating actions and the ability of accurately recommending videos for them.In implementing a real intelligent video recommendation system,a key problem we must solve is the design of some technologies that can acquire users’ ratings (which reveal their interests and preferences) in case of no rating actions.To acquire user’s implicit rating on videos automatically,this paper presented a neural-network-based method.Experimental results on samples about users’ video-viewing behavior and ratings show that this method is effective in gaining users’ implicit rating information.

Key words: Intelligent video recommendation,Personalized recommendation,Implicit rating acquisition,Neural network

[1] Lee T-Q,Park Y,Park Y-T.A similarity measure for collaborative filtering with implicit feedback[M]∥With Aspects of Artificial Intelligence.Berlin Heidelberg:Springer-Verlag,2007:385-397
[2] Lee T-Q,Park Y,Park Y-T.An empirical study on effectiveness of temporal information as implicit ratings [J].Expert systems with Applications,2009,36(2):1315-1321
[3] Claypool M,Le P,Waseda M,et al.Implicit interest indicators[C]∥Proceedings of the 6th International Conference on Intelligent User Interfaces,2001.New York:ACM,2001:33-40
[4] Nichols D.Implicit Rating and Filtering[C]∥Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering,1998.Budapest:ERCIM,1998:31-36
[5] Douglas O,Jinmook K.Implicit feedback for recommender systems[C]∥Proceedings of the AAAI Workshop on Recommender Systems,1998.AAAI,1998:81-83
[6] Nesrine Z.WebCap:Inferring the user's interests based on a real-time implicit feedback[C]∥Seventh International Conference on IEEE Digital Information Management,2012.IEEE,2012:62-67
[7] Hu Yi-fan,Yehuda K,Chris V.Collaborative filtering for implicitfeedback datasets[C]∥Eighth IEEE International Conference on Data Mining,2008.IEEE,2008:263-272
[8] Kai P,Jarkko S,Eerika S,et al.Combining eye movements and collaborative filtering for proactive information retrieval[C]∥Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2005.New York:ACM,2005:146-153
[9] 王继民,彭波.搜索引擎用户点击行为分析[J].情报学报,2006(2):154-162 Wang Ji-min,Peng Bo.User behavior analysis for a large-scale search engine [J].Journal of the China Society for Scientific and Technical Information,2006(2):154-162
[10] Shen Rui-min,Han Peng,Yang Fan,et al.An open framework for smart and personalized distance learning[M]∥Advances in Web-Based Learning.Berlin Heidelberg:Springer-Verlag,2002:19-30
[11] Jonathan H,Joseph K,John R.Explaining collaborative filtering recommendations[C]∥Proceedings of the 2000 ACM Confe-rence on Computer Supported Cooperative Work,2000.New York:ACM,2000:241-250
[12] Joseph K,Bradley M,David M,et al.GroupLens:applying collaborative filtering to Usenet news [J].Communications of the ACM,1997,40(3):77-87
[13] Zeno G,Steffen R,Christoph F,et al.MyMediaLite:A free re-commender system library[C]∥Proceedings of the fifth ACM conference on Recommender systems.New York:ACM,2011:305-308
[14] Masahiro M,Yoichi S.Information filtering based on user behavior analysis and best match text retrieval[C]∥Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.New York:Springer-Verlag,1994:272-281
[15] 张岚.基于学习行为的用户兴趣建模及应用研究[D].济南:山东大学,2012 Zhang Lan.Research on user interest model and application based on learning behaviors[D].Jinan:Shandong University,2012
[16] Keunho C,Donghee Y,Gunwoo K,et al.A hybrid online-pro-duct recommendation system:Combining implicit rating-based collaborative filtering and sequential pattern analysis [J].Electronic Commerce Research and Applications,2012,11(4):309-317
[17] Paul R,Neophytos I,Mitesh S,et al.GroupLens:an open architecture for collaborative filtering of net news[C]∥Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work.New York:ACM,1994:175-186
[18] Bamshad M,Robert C,Jaideep S.Automatic personalizationbased on Web usage mining[J].Communications of the ACM,2000,43(8):142-151
[19] Corin A,Pedro D,Daniel W.Personalizing web sites for mobile users[C]∥Proceedings of the 10th international conference on World Wide Web,2001.New York:ACM,2001:565-575
[20] John B,David H,Carl K.Empirical analysis of predictive algorithms for collaborative filtering[C]∥Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence,1998.Burlington:Morgan Kaufmann Publishers,1998:43-52
[21] Andrew S,Alexandrin P,Lyle U.Generative models for cold-start recommendations[C]∥Proceedings of the 2001 SIGIR Workshop on Recommender Systems,2001.2001
[22] Alexandrin P,Rin P,Lyle U,et al.Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments[C]∥Proceedings of the Seventeenth Confe-rence on Uncertainty in Artificial Intelligence.Burlington:Morgan Kaufmann Publishers,2001:437-444
[23] Andrew S,Alexandrin P,Lyle U.Methods and metrics for cold-start recommendations[C]∥Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval,2002.New York:ACM,2002:253-260
[24] Fábio S,Luiz A,Graca B.PersonalTVware:A proposal of architecture to support the context-aware personalized recommendation of TV programs[C]∥European Interactive TV Confe-rence,2009.Leuven,Belgium,2009
[25] Mahiye U,Zehra C,Esengul T.Content-based movie recommendation using different feature sets[C]∥Proceedings of the World Congress on Engineering and Computer Science.San Francisco,2012:517-521
[26] Zeno G,Steffen R,Lars S-T.Factorization models for context-/time-aware movie recommendations[C]∥Proceedings of the Workshop on Context-Aware Movie Recommendation,2010.New York:ACM,2010:14-19
[27] Steffen R,Christoph F,Zeno G,et al.BPR:Bayesian persona-lized ranking from implicit feedback[C]∥Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence.Arlington:AUAI Press,2009:452-461
[28] Denis P,Alexandros K,Xavier A,et al.Implicit feedback recommendation via implicit-to-explicit ordinal logistic regression mapping[C]∥Proceedings of the 3rd Workshop on Context-Aware Recommender Systems.2011
[29] 张立毅.神经网络盲均衡理论、算法与应用[M].北京:清华大学出版社,2013 Zhang Li-yi.Blind equalization theory,algorithm and application of neural network [M].Beijing:Tsinghua university press,2013

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!