Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup Design
Mashup editors enable end-users to mix the functionalities of several applications to derive a new one. However, when the end-user faces the development of a new mashup application s/he has to cope with the abundance of services and information sources available on the Web, and with complex operations like filtering and joining. Thus, even a simple to use mashup editor is not capable of providing adequate support, unless it embeds intelligent methods to process the semantics of available mashups and rank them based on how much they meet user needs. Most existing mashup editors process either semantic or statistical information to derive recommendations for the mashups considered suitable to user needs. However, none of them uses both strategies in a synergistic way. In this paper we present a new mashup advisory approach and a system that combines the statistical and semantic based approaches, by using collaborative filtering techniques and semantic tagging, in order to rank mashups based on user goals. We have proven the validity of the proposed approach through experimental sessions based on data from the ProgrammableWeb repository.
- 1.IBM mashup starter kit. 2007. http://www.alphaworks.ibm.com/tech/ibmmsk. Last accessed 12 June 2019.
- 2.Google mashup editor. 2008. https://developers.google.com/. Last accessed 12 June 2019.
- 3.Intel mash maker. 2008. https://software.intel.com/. Last accessed 12 June 2019.
- 4.Programmableweb. 2008. http://www.programmableweb.com. Last accessed 12 June 2019.
- 6.Baeza-Yates, R., B. Ribeiro-Neto, et al. 1999. Modern Information Retrieval, vol. 463. ACM Press New York.Google Scholar
- 7.Bellur, U., and H. Vadodaria. 2009. Web service ranking using semantic profile information. In Proceedings of the 2009 IEEE International Conference on Web Services, ICWS ’09, 872–879. IEEE Computer Society.Google Scholar
- 9.Bianchini, D., V.D. Antonellis, and M. Melchiori. 2010. A recommendation system for semantic mashup design. In Proceedings of the 23rd International Workshop on Database and Expert Systems Applications, DEXA ’10, 159–163. IEEE Computer Society.Google Scholar
- 11.Bianchini, D., V. De Antonellis, and M. Melchiori. 2010. Semantic-driven mashup design. In Proceedings of the 12th International Conference on Information Integration and Web-based Applications and Services, iiWAS ’10, 247–254. ACM.Google Scholar
- 15.D’Souza, C., V. Deufemia, A. Ginige, and G. Polese. 2018. Enabling the generation of web applications from mockups. Software: Practice and Experience 48 (4): 945–973.Google Scholar
- 16.Elmeleegy, H., A. Ivan, R. Akkiraju, and R. Goodwin. 2008. Mashup Advisor: A recommendation tool for mashup development. In Proceedings of the 2008 IEEE International Conference on Web Services, ICWS ’08, 337–344. IEEE Computer Society.Google Scholar
- 17.Goarany, K., G. Kulczycki, and M.B. Blake. 2010. Mining social tags to predict mashup patterns. In Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, SMUC ’10, 71–78. ACM.Google Scholar
- 19.Kolb, P. 2008. DISCO: A multilingual database of distributionally similar words. In Proceedings of KONVENS.Google Scholar
- 20.Lops, P., M. De Gemmis, and G. Semeraro. 2011. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, 73–105. Springer.Google Scholar
- 21.Maaradji, A., H. Hacid, R. Skraba, A. Lateef, J. Daigremont, and N. Crespi. 2011. Social-based web services discovery and composition for step-by-step mashup completion. In Proceedings of the 2011 IEEE International Conference on Web Services, ICWS ’11, 700–701. IEEE Computer Society.Google Scholar
- 22.Nakamura, A., and N. Abe. 1998. Collaborative filtering using weighted majority prediction algorithms. In Proceedings of the 15th International Conference on Machine Learning, ICML ’98, 395–403.Google Scholar
- 23.Ranabahu, A., M. Nagarajan, A. P. Sheth, and K. Verma. 2008. A faceted classification based approach to search and rank web APIs. In Proceedings of the 2008 IEEE International Conference on Web Services, ICWS ’08, 177–184. IEEE Computer Society.Google Scholar
- 25.Tapia, B., R. Torres, and H. Astudillo. 2011. Simplifying mashup component selection with a combined similarity- and social-based technique. In Proceedings of the 5th International Workshop on Web APIs and Service Mashups, Mashups ’11, 8:1–8:8. ACM.Google Scholar
- 27.Wu, Q., A. Iyengar, R. Subramanian, I. Rouvellou, I. Silva-Lepe, and T. Mikalsen. 2009. Combining quality of service and social information for ranking services. In Proceedings of the 7th International Joint Conference on Service-Oriented Computing, ICSOC-ServiceWave ’09, 561–575. Springer.Google Scholar
- 28.Zhao, C., C. Ma, J. Zhang, J. Zhang, L. Yi, and X. Mao. 2010. Hyperservice: Linking and exploring services on the web. In Proceedings of the 2010 IEEE International Conference on Web Services, ICWS ’10, 17–24.Google Scholar
- 29.Zheng, Z., H. Ma, M. Lyu, and I. King. 2009. WSRec: A collaborative filtering based web service recommender system. In Proceedings of the 2009 IEEE International Conference on Web Services, ICWS ’09, 437–444.Google Scholar