• MIN SHI Department of Computer Science and Engineering Hunan University of Science and Technology
  • JIANXUN LIU Department of Computer Science and Engineering Hunan University of Science and Technology
  • DONG ZHOU Department of Computer Science and Engineering Hunan University of Science and Technology
Keywords: tags, mashups, APIs, tag recommendation, topic model, PageRa


Tags have been extensively utilized to annotate Web services, which is beneficial to the management, classification and retrieval of Web service data. In the past, a plenty of work have been done on tag recommendation for Web services and their compositions (e.g. mashups). Most of them mainly exploit tag service matrix and textual content of Web services. In the real world, multiple relationships could be mined from the tagging systems, such as composition relationships between mashups and Application Programming Interfaces (APIs), and co-occurrence relationships between APIs. These auxiliary information could be utilized to enhance the current tag recommendation approaches, especially when the tag service matrix is sparse and in the absence of textual content of Web services. In this paper, we propose a hybrid approach for mashup tag recommendation. Our hybrid approach consists of two continuous processes: APIs selection and tags ranking. We first select the most important APIs of a new mashup based on a probabilistic topic model and a weighted PageRank algorithm. The topic model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to elicit the latent topic information. Then, tags of chosen important APIs are recommended to this mashup. In this process, a tag filtering algorithm has been employed to further select the most relevant and prevalent tags. The experimental results on a real world dataset prove that our approach outperforms several state-of-the-art methods.


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Font F., Serrà J., and Serra X. Analysis of the impact of a tag recommendation system in a real-world folksonomy. ACM Transactions on Intelligent Systems and Technology, 2015, 7(1): 6.

Si X.H., Sun M.S. Tag-LDA for scalable real-time tag recommendation. Information & Computational Science, 2009, 6(1): 23-31.

Aznag M. Multilabel learning for automatic web services tagging. Advanced Computer Science and Applications, 2014, 5(8): 4910– 4914.

Xie, H., Li, X., Wang, T., Lau, R. Y., Wong, T. L., Chen, L. and Li, Q. Incorporating sentiment into tag-based user profiles and resource profiles for personalized search in folksonomy. Information Processing & Management, 2016, 52(1): 61-72.

Xie, H., Li, Q., Mao, X., Li, X., Cai, Y. and Zheng, Q. Mining latent user community for tag-based and content-based search in social media. The Computer Journal, 2014, 57(9):1415-1430.

Lu F., W L.,Li M and Zhao J.F. Towards automatic tagging for web services//Proceedings of the 19th International Conference on Web Services. Honolulu, HI , 2012: 528- 535.

Gawinecki M., Cabri G., Paprzycki M. and Ganzha M. WSColab: Structured collaborative tagging for web service matchmaking// Proceedings of the International Conference on Web Information Systems and Technologies. Valencia, Spain, 2010: 70-77.

Lin, M., and Cheung D.W. Automatic tagging web services using machine learning techniques//Proceedings of the International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). Washington, DC, USA, 2014: 285-265.

Azmeh Z., Falleri J.R., Huchard M. and Tibermacine C. Automatic web service tagging using machine learning and wordNet synsets// Proceedings of the 6th Web Information Systems and Technologies. Valencia, Spain, 2010: 46-59.

Menezes G. V., Almeida J. M. and Belém F. Demand-driven tag recommendation. Machine Learning and Knowledge Discovery in Databases, 2010: 402-417.

Chen L., Wang Y., Yu Q., Zheng Z. and Wu J. WT-LDA: User tagging augmented LDA for web service clustering. Service-Oriented Computing, 2013: 162-176.

Wang H., Chen B., and Li W. Collaborative topic regression with social regularization for tag recommendation//Proceedings of the 23rd International Conference on Artificial Intelligence, Beijin, China, 2013: 2719–2725.

Wang H., Shi X.J and Yeung D. Relational stacked denoising autoencoder for tag recommendation//Proceedings of the 29th AAAI Conference on Artificial Intelligence. Austin, Texas, USA, 2015: 3052-3058.

Ralf K., Fankhauser P. and Nejdl W. Latent dirichlet allocation for tag recommendation//Proceedings of the third ACM conference on Recommender systems. New York, USA, 2009: 61-68.

Shi M, Liu J.X and Zhou D. A Probabilistic Topic Model for Mashup Tag Recommendation//Proceedings of International Conference on Web Services. Anchorage, AK, 2016: 444-451.

Liu Z.Y, Chen X.X. and Sun M.S. A simple word trigger method for social tag suggestion//Proceedings of the Association for Computational Linguistics, Stroudsburg, PA, USA, 2011: 1577-1588.

Zhao W, Guan Z, and Liu Z. Ranking on heterogeneous manifolds for tag recommendation in social tagging services. Neurocomputing, 2015(148): 521-534.

Gu B., Sheng V.S., and Li S. Bi-parameter space partition for cost-sensitive SVM//Proceedings of the 24th International Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015 :3532-3539.

Ma T., Zhou J., and Tang M. Social Network and Tag Sources Based Augmenting Collaborative Recommender System. IEICE Transactions on Information and Systems, 2015, 98(4) :902-910.

Wang M., Ni B., Hua X.-S., and Chua, T.-S. Assistive tagging: A survey of multimedia tagging with human-computer joint exploration. ACM Computing Surveys, 2012, 44(4): 25.

Belém F.M., Martins E.F, Almeida J.M, Gonçalves M.A. Personalized and object-centered tag recommendation methods for Web 2.0 applications. Information Processing & Management, 2014, 50(4): 524-553.

Chang Z. and Blei D.M. Relational topic models for document networks//Proceeding of the 12th international conference on Artificial Intelligence and Statistics. Florida, USA, 2009: 81-88.

Chang J. and Blei D. M. Hierarchical relational models for document networks. The Annals of Applied Statistics, 2010, 4(1): 124–150.

Wang, Y. and Stroulia, E. Semantic Structure Matching for Assessing Web-Service Similarity.// Proceeding of the 1st international Conference on Service-Oriented Computing, 2003 , 14 (4) :194-207.

Fernandez A., Hayes C., Loutas N., Peristeras V., Polleres A., and Tarabanis K. A. Closing the service discovery gap by collaborative tagging and clustering techniques//Proceedings of the 7th International Semantic Web Conference. Karlsruhe, Germany, 2008: 115-128.

Li C., Zhang R., Huai J.P. and Sun H.L. A novel approach for api recommendation in mashup development//Proceedings of International Conference on Web Services. Anchorage, AK, 2014:251–258.

Heinrich G. Parameter estimation for text analysis. Technical report, vsonix.GmbH and University of Leipzig, Germany, 2004.

Brin S. and Page L. The anatomy of a large-scale hypertextual web search engine. The Journal of Computer networks and ISDN systems, 1998, 30(1): 107–117.

Li W.J., Yeung D.Y., and Zhang Z.H. Probabilistic relational PCA//Proceedings of the 23rd Advances in neural information processing systems, Hyatt Regency, Vancouver Canada, 2009: 1123–1131.

Li W.J., Zhang Z.H., and Yeung D.Y. Latent wishart processes for relational kernel learning//Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, Clearwater Beach, Florida, USA, 2009: 336–343.