融合多源异构网络信息的标签推荐方法
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摘要
标签通常被广泛地应用于标注各种在线资源,例如文章、图像、电影等,其主要目的是便于用户理解以及高效地管理和检索海量网络资源。因为人工对这些海量资源进行标注十分繁琐且耗时,所以自动化标签推荐技术被广泛关注。目前大部分标签推荐方法主要通过挖掘资源的内容信息进行推荐。然而,现实世界中很多数据信息并非独立存在,如文献数据通过相互引用关系而形成复杂的网络结构。研究表明,资源的拓扑结构信息和文本内容信息可分别从2个不同角度对同一资源的语义特征进行概括,并且从2个方面观察到的信息可以互为补充和解释。基于此,提出一种同时对资源内容信息和资源网络拓扑结构信息进行统一建模的概率主题模型和标签推荐方法。该方法通过结合标签和资源内容之间的标注关系以及资源之间的链接关系等多源异构信息,去挖掘资源潜在的语义信息为新的资源推荐若干功能语义相近的标签。
Tags have been utilized extensively to associate various online resources, such as articles, images and movies, aiming at helping users understand and facilitate the process of managing and indexing huge web resources. Since it is time-consuming and prone for errors to create manual tags for these resources, automatic tag recommendation techniques have attracted widespread attention. At present, most tag recommendation methods mainly recommend tags by mining content information of resources. However, Most data information in the real world do not exist independently. For example, science articles have a complex network structure by referencing each other. The research show that the topology information and text content information of resources describe the similar semantic features of re-sources from two different perspectives, and the information from two aspects can complement and explain for each other. Based on this, we propose a probabilistic topic model and a tag recommendation method for simultaneously modeling content information and topology structure information of resource. This method uses multi-source heterogeneous information, such as tagging relationship between tag and resource content and link relationship between resources to mine potential semantic information of the resources to recommend several tags with similar functional semantics for the new resources. The experimental results on two real data sets prove the effectiveness of our proposed method.
Tags have been utilized extensively to associate various online resources, such as articles, images and movies, aiming at helping users understand and facilitate the process of managing and indexing huge web resources. Since it is time-consuming and prone for errors to create manual tags for these resources, automatic tag recommendation techniques have attracted widespread attention. At present, most tag recommendation methods mainly recommend tags by mining content information of resources. However, Most data information in the real world do not exist independently. For example, science articles have a complex network structure by referencing each other. The research show that the topology information and text content information of resources describe the similar semantic features of re-sources from two different perspectives, and the information from two aspects can complement and explain for each other. Based on this, we propose a probabilistic topic model and a tag recommendation method for simultaneously modeling content information and topology structure information of resource. This method uses multi-source heterogeneous information, such as tagging relationship between tag and resource content and link relationship between resources to mine potential semantic information of the resources to recommend several tags with similar functional semantics for the new resources. The experimental results on two real data sets prove the effectiveness of our proposed method.
引文
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[13] BAO Y, FANG H, ZHANG J. TopicMF: simultaneously exploiting ratings and reviews for recommendation[C]// Proceedings of the Twenty-eighth AAAI Conference on Artificial Intelligence. Québeca: AAAI Press, 2014: 2-8.
[14] BURKE R, VAHEDIAN F, MOBASHER B. Hybrid recommendation in heterogeneous networks[C]// Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. Aalborg: Springer, 2014: 49-60.
[15] AN S, ZHAO Z, ZHOU H. Research on an agent-based intelligent social tagging recommendation system[C]// Proceedings of International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou: IEEE, 2017: 43-46.
[16] 闫俊, 刘文飞, 林鸿飞. 基于标签混合语义空间的音乐推荐方法研究[J]. 中文信息学报, 2014, 28(4):117-122. YAN Jun, LIU Wenfei, LIN Hongfei. Music recommendation study based on tags multi-space[J]. Journal of Chinese Information Processing, 2014, 28(4):117-122.
[17] 于洪, 邓明瑶, 胡峰. 考虑用户标注状态的标签推荐方法[J]. 模式识别与人工智能, 2014, 27(8):673-682.YU Hong, DENG Mingyao, HU Feng. Tag recommendation method considering users tagging status[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(8):673-682.
[18] WANG H, SHI X, YEUNG D Y. Relational stacked denoising autoencoder for tag recommendation[C]// Proceedings of the Twenty-ninth AAAI Conference on Artificial Intelligence. Austin: AAAI Press, 2015: 3052-3058.
[19] KRESTEL R, FANKHAUSER P, NEJDL W. Latent dirichlet allocation for tag recommendation[C]// Proceedings of the third ACM conference on Recommender systems. New York: ACM, 2009: 61-68.
[20] SI X, SUN M. Tag-LDA for scalable real-time tag recommendation[J]. Journal of Computational Information Systems, 2008, 6(1):1-8.
[21] CHANG J, BLEI D M. Relational topic models for document networks[C]// Proceedings of the 12th International Conference on Artificial Intelligence and Statistics. Florida: JMLR, 2009: 81-88.
[22] WANG H, CHEN B, LI W J. Collaborative topic regression with social regularization for tag recommendation[C]// Proceedings of the Twenty-third International Joint Conference on Artificial Intelligence. Beijing: AAAI Press, 2013: 2719-2725.
(1)Delicious, http://del.icio.us/
(2)CiteULike, http://www.citeulike.org/
[2] BELéM F M, ALMEIDA J M, GON?ALVES M A. A survey on tag recommendation methods[J]. Journal of the Association for Information Science and Technology, 2017, 68(4):830-844.
[3] ZHAO W, GUAN Z Y, LIU Z. Ranking on heterogeneous manifolds for tag recommendation in social taggingservices[J]. Neurocomputing, 2015, 148:521-534.
[4] BENZARTI M A, CHIDLOVSKII B, VIJAYAKUMAR N. Local metric learning for tag recommendation in social networks using indexing.:US,9600826[P], 2017.
[5] GUO Z, ZHANG Z M, ZHU S H, et al. A two-level topic model towards knowledge discovery from citation networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(4):780-794.
[6] WEST J D, WESLEY-SMITH I, BERGSTROM C T. A recommendation system based on hierarchical clustering of an article-level citation network[J]. IEEE Transactions on Big Data, 2016, 2(2):113-123.
[7] WANG M, NI B B, HUA X S, et al. Assistive tagging: a survey of multimedia tagging with human-computer joint exploration[J]. ACM Computing Surveys, 2012, 44(4):1-24.
[8] 褚晓敏, 王中卿, 朱巧明, 等. 基于简介和评论的标签推荐方法研究[J]. 中文信息学报, 2015, 29(6):179-184. CHU Xiaomin, WANG Zhongqing, ZHU Qiaoming, et al. Tag recommendation with summary and comment information[J]. Journal of Chinese Information Processing, 2015, 29(6):179-184.
[9] LIU Z, CHEN X, SUN M. A simple word trigger method for social tag suggestion[C]// Proceedings of the Conference on Empirical Methods in Natural Language Processing. Edinburgh: Association for Computational Linguistics, 2011: 1577-1588.
[10] BELéM F M, MARTINS E F, ALMEIDA J M, et al. Personalized and object-centered tag recommendation methods for Web 2.0 applications[J]. Information Processing & Management, 2014, 50(4):524-553.
[11] LI W, YEUNG D Y. Social relations model for collaborative filtering[C]// Proceedings of the Twenty-fifth AAAI Conference on Artificial Intelligence. San Francisco: AAAI Press, 2011: 803-808.
[12] LOPS P, DE GEMMIS M, SEMERARO G, et al. Content-based and collaborative techniques for tag recommendation: an empirical evaluation[J]. Journal of Intelligent Information Systems, 2013, 40(1):41-61.
[13] BAO Y, FANG H, ZHANG J. TopicMF: simultaneously exploiting ratings and reviews for recommendation[C]// Proceedings of the Twenty-eighth AAAI Conference on Artificial Intelligence. Québeca: AAAI Press, 2014: 2-8.
[14] BURKE R, VAHEDIAN F, MOBASHER B. Hybrid recommendation in heterogeneous networks[C]// Proceedings of the International Conference on User Modeling, Adaptation, and Personalization. Aalborg: Springer, 2014: 49-60.
[15] AN S, ZHAO Z, ZHOU H. Research on an agent-based intelligent social tagging recommendation system[C]// Proceedings of International Conference on Intelligent Human-Machine Systems and Cybernetics. Hangzhou: IEEE, 2017: 43-46.
[16] 闫俊, 刘文飞, 林鸿飞. 基于标签混合语义空间的音乐推荐方法研究[J]. 中文信息学报, 2014, 28(4):117-122. YAN Jun, LIU Wenfei, LIN Hongfei. Music recommendation study based on tags multi-space[J]. Journal of Chinese Information Processing, 2014, 28(4):117-122.
[17] 于洪, 邓明瑶, 胡峰. 考虑用户标注状态的标签推荐方法[J]. 模式识别与人工智能, 2014, 27(8):673-682.YU Hong, DENG Mingyao, HU Feng. Tag recommendation method considering users tagging status[J]. Pattern Recognition and Artificial Intelligence, 2014, 27(8):673-682.
[18] WANG H, SHI X, YEUNG D Y. Relational stacked denoising autoencoder for tag recommendation[C]// Proceedings of the Twenty-ninth AAAI Conference on Artificial Intelligence. Austin: AAAI Press, 2015: 3052-3058.
[19] KRESTEL R, FANKHAUSER P, NEJDL W. Latent dirichlet allocation for tag recommendation[C]// Proceedings of the third ACM conference on Recommender systems. New York: ACM, 2009: 61-68.
[20] SI X, SUN M. Tag-LDA for scalable real-time tag recommendation[J]. Journal of Computational Information Systems, 2008, 6(1):1-8.
[21] CHANG J, BLEI D M. Relational topic models for document networks[C]// Proceedings of the 12th International Conference on Artificial Intelligence and Statistics. Florida: JMLR, 2009: 81-88.
[22] WANG H, CHEN B, LI W J. Collaborative topic regression with social regularization for tag recommendation[C]// Proceedings of the Twenty-third International Joint Conference on Artificial Intelligence. Beijing: AAAI Press, 2013: 2719-2725.
(1)Delicious, http://del.icio.us/
(2)CiteULike, http://www.citeulike.org/