评价数据中的用户偏好建模:一种基于隐变量模型的方法
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摘要
互联网应用中的评价数据包含丰富的用户观点和偏好信息.为了能更准确地发现用户偏好,综合考虑用户的评分和评论数据,基于贝叶斯网提出了一种针对评价数据的用户偏好建模方法.首先给出了从评论数据中抽取不同评论属性的方法,然后分别从评分和评论出发确定了用户偏好模型的初始结构约束和初始参数约束,最后给出了基于约束条件的用户偏好建模方法.实验结果表明,与单独评分或者评论数据构建的用户偏好模型相比,综合考虑评分和评论数据的用户偏好模型能更准确地估计用户偏好.
Rating data of Internet applications contain abundant information about users' opinions and preferences. In order to discover user preference more accurately, this paper proposes a user preference modeling approach upon rating data based on Bayesian network, considering users' rating score and review data. First, the method for extracting different review attributes from the review data is presented. Then, the initial structure constraints and parameter constraints of the user preference model are selected from score and review. Finally, a user preference modeling method based on constraints is presented. Experimental results show that, compared with the user preference model constructed by score or review data alone, the user preference model which considers both score and review data can estimating user preference more accurately.
Rating data of Internet applications contain abundant information about users' opinions and preferences. In order to discover user preference more accurately, this paper proposes a user preference modeling approach upon rating data based on Bayesian network, considering users' rating score and review data. First, the method for extracting different review attributes from the review data is presented. Then, the initial structure constraints and parameter constraints of the user preference model are selected from score and review. Finally, a user preference modeling method based on constraints is presented. Experimental results show that, compared with the user preference model constructed by score or review data alone, the user preference model which considers both score and review data can estimating user preference more accurately.
引文
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[3]Mcauley J,Leskovec J.Hidden factors and hidden topics:Understanding rating dimensions with review text[C].ACM Conference on Recommender Systems,ACM,New York,NY,USA,2013:165-172.
[4]Wang H N,Yue L,Zhai C X.Latent aspect rating analysis on review text data:a rating regression approach[C].ACM SIGKDD International Conference on Knowledge Discovery&Data Mining,ACM,New York,NY,USA,2010:783-792.
[5]Bao Y,Fang H,Zhang J.TopicMF:simultaneously exploiting ratings and reviews for recommendation[C].Twenty-Eighth AAAI Conference on Artificial Intelligence,Québec,Canada,2014:2-8.
[6]郭心宇,岳昆,李劲,等.面向评价数据中用户偏好发现的证据理论方法[J].计算机科学与探索,2017,11(2):231-241.Guo X Y,Yue K,Li J,et al.Evidence-theory approach for discovering user preferences in rating data[J].Journal of Frontiers of Computer Science and Technology,2017,11(2):231-241.
[7]Diao Q,Qiu M,Wu C Y,et al.Jointly modeling aspects,ratings and sentiments for movie recommendation[C].Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,New York,USA,2014:193-202.
[8]Hu M,Liu B.Mining and summarizing customer reviews[C].Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,Seattle,Washington,USA,2004:168-177.
[9]Jing L P,Huang H K,Shi H B.Improved feature selection approach TFIDF in text mining[C].International Conference on Machine Learning&Cybernetics,2003:944-946.
[10]Wu X K,Cheng B,Chen J L.Collaborative filtering service recommendation based on a novel similarity computation method[J].IEEE Transactions on Services Computing,2017,10(3):352-365.DOI:1 0.1109/TSC.2015.2479228.
[11]Musat C C,Liang Y,Faltings B.Recommendation using textual opinions[C].International Joint Conference on Artificial Intelligence,Beijing,China,2013:2 684-2 690.
[12]Mikolov T,Sutskever I,Chen K,et al.Distributed representations of words and phrases and their compositionality[J].Advances in Neural Information Processing Systems,2013,26:3 111-3 119.
[13]Mikolov T,Yih W,Zweig G.Linguistic regularities in continuous space word representations[C].Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,Atlanta,Georgia,2013:746-751.
[14]张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006.Zhang L W,Guo H P.Introduction to Bayesian networks[M].Beijing:Science Press,2006.
[15]Wen L,Ping L D,Wu C M,et al.Distributed Bayesian network trust model in virtual network[C].2nd International Conference on Networks Security.Wuhan,China,2010:71-74.DOI:10.1109/nsuctc.L010.153.
[16]王飞,岳昆,孙正宝,等.基于贝叶斯网的评价数据分析和动态行为建模[J].计算机研究与发展,2017,54(7):1 488-1 499.Wang F,Yue K,Sun Z B,et al.Analyzing rating data and modeling dynamic behaviors of users based on the Bayesian network[J].Journal of Computer Research and Development,2017,54(7):1 488-1 499.
[17]Guo S Q,Yu Z F,Deng F,et al.Hierarchical Bayesian inference and learning in spiking neural networks[J].IEEE Transactions on Cybernetics,2017:133-145.
[18]Gao R S,Yue K,Wu H,et al.Modeling user preference from rating data based on the Bayesian network with a latent variable[C].International Conference on Web-Aged Information Management,Nanchang,China,2016:3-16.
[19]Cheng Z Y,Shen J L.On effective location-aware music recommendation[J].ACM Transactions on Information Systems,2016,34(2):1-13.
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