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Computational Intelligence for Semantic Knowledge Management pp 25–37Cite as

Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup Design

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 837))

Abstract

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.

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References

  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.

  5. Adomavicius, G., and A. Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge & Data Engineering 6: 734–749.

    Article  Google Scholar 

  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 

  8. Benslimane, D., S. Dustdar, and A. Sheth. 2008. Services mashups: The new generation of web applications. IEEE Internet Computing 12 (5): 13–15.

    Article  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 

  10. D. Bianchini, V.D. Antonellis, and M. Melchiori. 2017. WISeR: A multi-dimensional framework for searching and ranking web APIs. ACM Transactions on the Web 11 (3): 19:1–19:32.

    Article  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 

  12. Caruccio, L., V. Deufemia, and G. Polese. 2015. Understanding user intent on the web through interaction mining. Journal of Visual Languages & Computing 31: 230–236.

    Article  Google Scholar 

  13. Caruccio, L., G. Polese, and G. Tortora. 2016. Synchronization of queries and views upon schema evolutions: A survey. ACM Transactions on Database Systems (TODS) 41 (2): 9.

    Article  MathSciNet  Google Scholar 

  14. Chen, H., B. Lu, Y. Ni, G. Xie, C. Zhou, J. Mi, and Z. Wu. 2009. Mashup by surfing a web of data apis. Proceedings of the VLDB Endowment 2 (2): 1602–1605.

    Article  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 

  18. Greenshpan, O., T. Milo, and N. Polyzotis. 2009. Autocompletion for mashups. Proceedings of the VLDB Endowment 2 (1): 538–549.

    Article  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 

  24. Schall, D., H.-L. Truong, and S. Dustdar. 2008. Unifying human and software services in web-scale collaborations. IEEE Internet Computing 12 (3): 62–68.

    Article  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 

  26. Wang, G., Y. Han, Z. Zhang, and S. Zhang. 2015. A dataflow-pattern-based recommendation framework for data service mashup. IEEE Transactions on Services Computing 8 (6): 889–902.

    Article  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 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Loredana Caruccio, Vincenzo Deufemia & Giuseppe Polese

  2. Relatech s.r.l., Centro Direzionale, Isola E5, 80143, Naples, Italy

    Salvatore Esposito

Authors
  1. Loredana Caruccio
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  2. Vincenzo Deufemia
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  3. Salvatore Esposito
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  4. Giuseppe Polese
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Corresponding author

Correspondence to Vincenzo Deufemia .

Editor information

Editors and Affiliations

  1. Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy

    Giovanni Acampora

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  3. Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Skellefteå, Sweden

    Athanasios V. Vasilakos

  4. Department of Physics “Ettore Pancini”, University of Naples Federico II, Naples, Italy

    Autilia Vitiello

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Caruccio, L., Deufemia, V., Esposito, S., Polese, G. (2020). Combining Collaborative Filtering and Semantic-Based Techniques to Recommend Components for Mashup Design. In: Acampora, G., Pedrycz, W., Vasilakos, A., Vitiello, A. (eds) Computational Intelligence for Semantic Knowledge Management. Studies in Computational Intelligence, vol 837. Springer, Cham. https://doi.org/10.1007/978-3-030-23760-8_2

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