基于流形对齐的论坛个性化推荐与检索
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摘要
用户越来越习惯在目前流行的论坛社区等网站上进行知识分享,沟通与阅读有趣文章。然而,在论坛大量的内容中,用户却很难在信息过载的情况下找到他们感兴趣的帖子。有两个原因导致传统的个性化推荐系统并不能直接运用在论坛中。一个是论坛网站不像电影与音乐网站那样用户会对产品进行评分；第二个则是由于论坛中用户看帖不回的记录较多,稀疏问题更加严重。另外,检索系统在论坛中的应用也存在问题,在论坛用同一条目检索出的帖子不会使得每个人满意,因此一个个性化的检索也是论坛需要的功能。本文通过挖掘用户回复信息与帖子语义信息,提出了一个可以将用户和帖子映射到同一子空间中表达的算法：用户-帖流形对齐降维。在用户-帖的共有流形中,用户与帖子之间的联系可以方便表达,离目标用户距离最近的就是用户感兴趣的帖子,可以方便地进行推荐。本文还通过用户-帖的流形对齐降维结果对用户兴趣进行建模,进而设计了个性化检索系统,并将用户的反馈信息也集成到检索系统中。在digg. com数据集上做的实验评估表明了本文设计的个性化推荐与检索系统性能非常出色。
People are more and more willing to participate in online forums to share their knowledge and experience. However, it may not be easy for them to find their desired threads in online forums due to the information overload problem. Traditional recommendation approaches can not be directly applied to online forums due to two reasons. First, unlike traditional movie or music recommendation problem, there is no rating information in online forums. Second, the sparsity problem is more severe since the users may only read threads but take no-actions. In addition, retrieval system in online forum will probably give results that not interest everybody. Thus, a personalized retrieval answer will be more preferred. To address these limitations, in this paper we propose to make use of the reply relationships among users, as well as thread contents. A learning algorithm is introduced to infer a user-thread alignment manifold in which both users and thread contents can be well represented. Thus, the relatedness between users and threads can be measured on this alignment manifold, and the closest threads which can best meet the corresponding user's information needs are recommended. With the advance of user-thread alignment manifold, we can also capture user interests as well as user feedbacks to make personalized retrieval system. Experiments on a dataset crawled from digg.com have demonstrated the superiority of our personalized recommendation and retrieval system.
People are more and more willing to participate in online forums to share their knowledge and experience. However, it may not be easy for them to find their desired threads in online forums due to the information overload problem. Traditional recommendation approaches can not be directly applied to online forums due to two reasons. First, unlike traditional movie or music recommendation problem, there is no rating information in online forums. Second, the sparsity problem is more severe since the users may only read threads but take no-actions. In addition, retrieval system in online forum will probably give results that not interest everybody. Thus, a personalized retrieval answer will be more preferred. To address these limitations, in this paper we propose to make use of the reply relationships among users, as well as thread contents. A learning algorithm is introduced to infer a user-thread alignment manifold in which both users and thread contents can be well represented. Thus, the relatedness between users and threads can be measured on this alignment manifold, and the closest threads which can best meet the corresponding user's information needs are recommended. With the advance of user-thread alignment manifold, we can also capture user interests as well as user feedbacks to make personalized retrieval system. Experiments on a dataset crawled from digg.com have demonstrated the superiority of our personalized recommendation and retrieval system.
引文
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[16]张海鹏,李烈彪,李仙,周亚蜂.基于项目分类预测的协同过滤推荐算法[J].情报学报,2008,27(2)：218-223
[17]张忠平,郭献丽.一种优化的基于项目评分预测的协同过滤推荐算法[J].计算机应用研究,2009,25(9)：2658-2660
[18]曾艳,麦永浩.基于内容预测和项目评分的协同过滤推荐[J].计算机应用,2004,24(1)：111-113
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[21]李涛,王建东.基于非负矩阵分解的隐私保护协同过滤算法[J].信息与控制,2008,37(6)：660-664
[22]彭玉.基于用户生活方式的协同过滤推荐算法[J].电脑知识与技术,2009,5(9)：334-348
[23]彭德巍,胡斌.一种基于用户特征和时间的协同过滤算法[J].武汉理工大学学报,2009,31(3)：24-28
[24]王辉,高利军,王听忠.个性化服务中基于用户聚类的协同过滤推荐[J].计算机应用,2007,27(5)：1225-1227
[25]李涛,王建东,叶飞跃,冯新宇,张有东.一种基于用户聚类的协同过滤推荐算法[J].系统工程与电子技术,2007,29(7),1178-1182
[26]G. Karypis. Evaluation of item-based top-N recommendation algorithms[C]. Proceedings of the 10th international conference on Information and knowledge management. Atlanta, Georgia, USA:ACM,2001:247-254
[27]J. Glen and W. Jennifer. Scaling personalized web search[C]. Proceedings of the 12th international conference on World Wide Web,2003:271-279
[28]K. Diane and T. Jaime. Implicit feedback for inferring user preference[C]. ACM SIGIR Forum,2003,37 (2):18-28
[29]G. Salton and C. Buckley. Improving retrieval performance by retrieval feedback[J]. Journal of the American Society for Information Science,1990, 41(4):288-297
[30]曹红兵.搜索引擎的个性化检索研究[J].图书情报工作,2007,53(9)：762-775
[31]周林莲.浅议搜索引擎的个性化——以UJIKO为例[J].湖北广播电视大学学报,2008,28(9)：578-590
[32]李慧,李存华,王霞.一种新颖的个性化视频搜索排名算法[J].南京师范大学学报,2008,8(4)：367-378
[33]A. Singhal. Modern information retrieval:A brief overview[J]. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering,2001: 24(4):35-43
[34]K. Sugiyama, K. Hatano, M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users[C]. Proceedings of the 13th international conference on World Wide Web,2004:675-684
[35]M. Speretta and S. Gauch. Personalized search based on user search histories[C]. In Proceeding of the IEEE International Conference on Web Intelligence, 2005:622-628
[36]M. Chau, D. Zeng, H. Chen. Personalized spiders for web search and analysis[C]. In Proceedings of the 1st ACM-IEEE Joint Conference on Digital Libraries, 2001:79-87
[37]周谆,杨炳儒.基于认知的流形学习方法概要.计算机科学,2009,36(5)：234-237
[38]R.O. Duda, RE. Hart, and D.G. Stork. Pattern Classification, second ed. Wiley-Interscience,2000:423-445
[39]Z. Su, S. Li, and H.-J. Zhang. Extraction of Feature Subspace for Content-Based Retrieval Using Relevance Feedback[C]. Proc. Ninth Ann. ACM Int'l Conf. Multimedia (Multimedia'01),2001:98-106
[40]J. Tenenbaum, V. de Silva, and J. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science,2000,290(500):2319-2323
[41]S. Roweis and L. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science,2000,290(500):2323-2326
[42]M. Belkin and P. Niyogi. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering[C]. Advances in Neural Information Processing Systems 14,2001:585-591
[43]X. He, D. Cai, and W. Min. Statistical and Computational Analysis of LPP[C]. Proc.22nd Int'l Conf. Machine Learning (ICML'05),2005:281-288
[44]X. He and P. Niyogi. Locality Preserving Projections[C]. Advances in Neural Information Processing Systems 16,2003:153-161
[45]V. G'omez, A. Kaltenbrunner, and V. L'opez. Statistical analysis of the social network and discussion threads in Slashdot[C]. In Proceeding of the 17th international conference on World Wide Web,2008:645-654
[46]X. He, D. Cai, H. Liu, and W.-Y. Ma. Locality preserving indexing for document representation[C]. In Proc.2004 Int. Conf. on Research and Development in Information Retrieval (SIGIR'04),2004:96-103
[47]G. Strang. Introduction to linear algebra. Wellesley Cambridge Press, 2003:748-789
[48]F. Chung. Spectral graph theory.1997:324-367
[49]J. Rocchio. Relevance feedback in information retrieval [C]. In The SMART Retrieval System-Experiments in Automatic Document Processing,1971: 313-323
[50]R. Pon, A. Cardenas, D. Buttler, and T. Critchlow. Tracking multiple topics for finding interesting articles[C]. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining,2007: 560-569
[51]D.L. Swets and J. Weng. Using Discriminant Eigenfeatures for Image Retrieval[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1996,18(8):831-836
[52]M. Belkin, P. Niyogi, and V. Sindhwani. Manifold Regularization:A Geometric Framework for Learning from Examples[J]. J. Machine Learning Research,2006: 2399-2434
[53]Y.-Y. Lin, T.-L. Liu, and H.-T. Chen. Semantic Manifold Learning for Image Retrieval[C]. Proc.13th Ann. ACM Int'l Conf. Multimedia (Multimedia'05), 2005:249-258
[54]W.-Y. Ma and B.S. Manjunath. Netra:A Toolbox for Navigating Large Image Databases[J]. Multimedia Systems,1999,7(3):184-198
[2]G. Adomavicius, A. Tuzhili. Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749
[3]A. Anagnostopoulos, R. Kumar, and M. Mahdian. Influence and correlation in social networks [C]. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining,2008:7-15
[4]Y. Matsuo and H. Yamamoto. Community gravity:measuring bidirectional effects by trust and rating on online social networks[C]. In Proceedings of the 18th international conference on World wide web,2009:751-760
[5]J. Golbeck. Trust and nuanced profile similarity in online social networks[J]. ACM Transactions on the Web (TWEB),2009,3(4):1-33
[6]C. Ziegler and J. Golbeck. Investigating interactions of trust and interest similarity. Decision support systems[C],2007,43(2):460-475
[7]P. Massa and P. Avesani. Trust-aware recommender systems[C]. In Proceedings of the 2007 ACM conference on Recommender systems,2007:24
[8]M. Jamali and M. Ester. TrustWalker:a random walk model for combining trust-based and item-based recommendation[C]. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009:397-406
[9]H. Ma, I. King, and M. Lyu. Learning to recommend with social trust ensemble[C]. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval,2009:203-210
[10]J. Breese, D. Hecherman, C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering[C]. In:Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence(UAI’98),1998:43-52
[11]姚忠,魏佳,吴跃.基于高维稀疏数据聚类的协同过滤推荐算法[J].信息系统学报,2008：212-222
[12]娄建玮,刘红军,郑伟.C#/SQL实现基于项目评分预测的推荐算法[J].职大学报,2007：22-23
[13]邓爱林,朱扬勇,施伯乐.基于项目评分预测的协同过滤推荐算法[J].软件学报,2003,14(9)：1621-1629
[14]董祥和,齐莉丽,董荣和.优化的协作过滤推荐算法[J].计算机工程与应用,2009,45(8)：229-232
[15]周军锋,汤显,郭景峰.一种优化的协同过滤推荐算法[J].计算机研究与发展,2004,41(8)：1842-1847
[16]张海鹏,李烈彪,李仙,周亚蜂.基于项目分类预测的协同过滤推荐算法[J].情报学报,2008,27(2)：218-223
[17]张忠平,郭献丽.一种优化的基于项目评分预测的协同过滤推荐算法[J].计算机应用研究,2009,25(9)：2658-2660
[18]曾艳,麦永浩.基于内容预测和项目评分的协同过滤推荐[J].计算机应用,2004,24(1)：111-113
[19]B. Sarwar, G. Karypis, J. Konstan, J. Riedl. Item-Based collaborative filtering recommendation algorithms[C]. In:Proceedings of the 10th International World Wide Web Conference,2001:285-295
[20]D. Lee, H. Seung. Learning the parts of objects by Non-negative matrix factorization[J]. Nature,1999,401(6755):788-791
[21]李涛,王建东.基于非负矩阵分解的隐私保护协同过滤算法[J].信息与控制,2008,37(6)：660-664
[22]彭玉.基于用户生活方式的协同过滤推荐算法[J].电脑知识与技术,2009,5(9)：334-348
[23]彭德巍,胡斌.一种基于用户特征和时间的协同过滤算法[J].武汉理工大学学报,2009,31(3)：24-28
[24]王辉,高利军,王听忠.个性化服务中基于用户聚类的协同过滤推荐[J].计算机应用,2007,27(5)：1225-1227
[25]李涛,王建东,叶飞跃,冯新宇,张有东.一种基于用户聚类的协同过滤推荐算法[J].系统工程与电子技术,2007,29(7),1178-1182
[26]G. Karypis. Evaluation of item-based top-N recommendation algorithms[C]. Proceedings of the 10th international conference on Information and knowledge management. Atlanta, Georgia, USA:ACM,2001:247-254
[27]J. Glen and W. Jennifer. Scaling personalized web search[C]. Proceedings of the 12th international conference on World Wide Web,2003:271-279
[28]K. Diane and T. Jaime. Implicit feedback for inferring user preference[C]. ACM SIGIR Forum,2003,37 (2):18-28
[29]G. Salton and C. Buckley. Improving retrieval performance by retrieval feedback[J]. Journal of the American Society for Information Science,1990, 41(4):288-297
[30]曹红兵.搜索引擎的个性化检索研究[J].图书情报工作,2007,53(9)：762-775
[31]周林莲.浅议搜索引擎的个性化——以UJIKO为例[J].湖北广播电视大学学报,2008,28(9)：578-590
[32]李慧,李存华,王霞.一种新颖的个性化视频搜索排名算法[J].南京师范大学学报,2008,8(4)：367-378
[33]A. Singhal. Modern information retrieval:A brief overview[J]. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering,2001: 24(4):35-43
[34]K. Sugiyama, K. Hatano, M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users[C]. Proceedings of the 13th international conference on World Wide Web,2004:675-684
[35]M. Speretta and S. Gauch. Personalized search based on user search histories[C]. In Proceeding of the IEEE International Conference on Web Intelligence, 2005:622-628
[36]M. Chau, D. Zeng, H. Chen. Personalized spiders for web search and analysis[C]. In Proceedings of the 1st ACM-IEEE Joint Conference on Digital Libraries, 2001:79-87
[37]周谆,杨炳儒.基于认知的流形学习方法概要.计算机科学,2009,36(5)：234-237
[38]R.O. Duda, RE. Hart, and D.G. Stork. Pattern Classification, second ed. Wiley-Interscience,2000:423-445
[39]Z. Su, S. Li, and H.-J. Zhang. Extraction of Feature Subspace for Content-Based Retrieval Using Relevance Feedback[C]. Proc. Ninth Ann. ACM Int'l Conf. Multimedia (Multimedia'01),2001:98-106
[40]J. Tenenbaum, V. de Silva, and J. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction[J]. Science,2000,290(500):2319-2323
[41]S. Roweis and L. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding[J]. Science,2000,290(500):2323-2326
[42]M. Belkin and P. Niyogi. Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering[C]. Advances in Neural Information Processing Systems 14,2001:585-591
[43]X. He, D. Cai, and W. Min. Statistical and Computational Analysis of LPP[C]. Proc.22nd Int'l Conf. Machine Learning (ICML'05),2005:281-288
[44]X. He and P. Niyogi. Locality Preserving Projections[C]. Advances in Neural Information Processing Systems 16,2003:153-161
[45]V. G'omez, A. Kaltenbrunner, and V. L'opez. Statistical analysis of the social network and discussion threads in Slashdot[C]. In Proceeding of the 17th international conference on World Wide Web,2008:645-654
[46]X. He, D. Cai, H. Liu, and W.-Y. Ma. Locality preserving indexing for document representation[C]. In Proc.2004 Int. Conf. on Research and Development in Information Retrieval (SIGIR'04),2004:96-103
[47]G. Strang. Introduction to linear algebra. Wellesley Cambridge Press, 2003:748-789
[48]F. Chung. Spectral graph theory.1997:324-367
[49]J. Rocchio. Relevance feedback in information retrieval [C]. In The SMART Retrieval System-Experiments in Automatic Document Processing,1971: 313-323
[50]R. Pon, A. Cardenas, D. Buttler, and T. Critchlow. Tracking multiple topics for finding interesting articles[C]. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining,2007: 560-569
[51]D.L. Swets and J. Weng. Using Discriminant Eigenfeatures for Image Retrieval[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1996,18(8):831-836
[52]M. Belkin, P. Niyogi, and V. Sindhwani. Manifold Regularization:A Geometric Framework for Learning from Examples[J]. J. Machine Learning Research,2006: 2399-2434
[53]Y.-Y. Lin, T.-L. Liu, and H.-T. Chen. Semantic Manifold Learning for Image Retrieval[C]. Proc.13th Ann. ACM Int'l Conf. Multimedia (Multimedia'05), 2005:249-258
[54]W.-Y. Ma and B.S. Manjunath. Netra:A Toolbox for Navigating Large Image Databases[J]. Multimedia Systems,1999,7(3):184-198