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A recommender system based on historical usage data for web service discovery

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Abstract

The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user’s perspective, languages, and notations, which easily decrease recommendation’s efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user’s interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.

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References

  1. Birukou A, Blanzieri E, D’Andrea V, Giorgini P, Kokash N (2007) Improving web service discovery with usage data. Softw IEEE 24(6): 47–54. doi:10.1109/MS.2007.169

    Article  Google Scholar 

  2. Blake MB, Nowlan MF (2007) A web service recommender system using enhanced syntactical matching. In: ICWS. pp 575–582

  3. Breese JS, Heckerman D, Kadie C (1998) Empirical analysis of predictive algorithms for collaborative filtering. Morgan Kaufmann, Los Altos, pp 43–52

    Google Scholar 

  4. Chen AYA, McLeod D (2006) Collaborative filtering for information recommendation systems. Encyclopedia of E-Commerce, E-Government, and Mobile Commerce, IGI Global pp 118–123

  5. Dahlen B, Konstan J, Herlocker J, Good N, Borchers A, Riedl J (1998) Jump-starting movieLens: User benefits of starting a collaborative filtering system with “dead-data”. In: University of Minnesota TR 98-017

  6. Daniel B, Katharina S, Holger L, Fensel D (2006) Web service discovery? A reality check. 3rd European Semantic Web Conference, Budva, Montenegro (June 2006)

  7. Dong X, Halevy A, Madhavan J, Nemes E, Zhang J (2004) Similarity search for web services. In: VLDB ’04: Proceedings of the thirtieth international conference on very large data bases. VLDB Endowment. pp 372–383

  8. Ferris C, Farrell J (2003) What are web services?. Commun ACM 46(6): 31. doi:10.1145/777313.777335

    Article  Google Scholar 

  9. Herlocker JL, Konstan JA, Riedl J (2000) Explaining collaborative filtering recommendations. In: Proceedings of the 2000 ACM conference on computer supported cooperative work, CSCW ’00. ACM, New York, NY, USA, pp 241–250

  10. Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22: 5–53

    Article  Google Scholar 

  11. Hill W, Stead L, Rosenstein M, Furnas G (1995) Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’95. ACM Press/Addison-Wesley, New York, NY, USA, pp 194–201

  12. Kokash N, Birukou A, D’Andrea V (2007) Web service discovery based on past user experience. In: Proceedings of the 10th international conference on business information systems, BIS’07. Springer, Berlin, Heidelberg, pp 95–107

  13. Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J, Volume H (1997) Grouplens: applying collaborative filtering to usenet news. Commun ACM 40: 77–87

    Article  Google Scholar 

  14. Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. Internet Comput IEEE 7(1): 76–80. doi:10.1109/MIC.2003.1167344

    Article  Google Scholar 

  15. Linden GD, Jacobi JA, Benson EA (2001) Collaborative recommendations using item-to-item similarity mappings. Patent and Trademark Office, Washington. http://www.patentlens.net/patentlens/patent/US_6266649/en/

  16. Ma J, Zhang Y, He J (2008) Web services discovery based on latent semantic approach. In: ICWS ’08: Proceedings of the 2008 IEEE international conference on web services. IEEE Computer Society, Washington, DC, USA, pp 740–747. doi:10.1109/ICWS.2008.135

  17. Manikrao US, Prabhakar TV (2005) Dynamic selection of web services with recommendation system. In: NWESP ’05: Proceedings of the international conference on next generation web services practices. IEEE Computer Society, Washington, DC, USA, p 117. doi:10.1109/NWESP.2005.32

  18. Manning CD, Raghavan P, Schutze H (2008) Introduction to information retrieval. Cambridge University Press, New York

    MATH  Google Scholar 

  19. Paolucci M, Kawamura T, Payne TR, Sycara KP (2002) Semantic matching of web services capabilities. In: ISWC ’02: Proceedings of the first international semantic web conference on the semantic web. Springer, London, UK, pp 333–347

  20. Perez L, Barranco M, Martinez L (2007) Building user profiles for recommender systems from incomplete preference relations. Fuzzy Syst Conf 2007. FUZZ-IEEE 2007. IEEE International, pp 1–6. doi:10.1109/FUZZY.2007.4295499

  21. Platzer C, Dustdar S (2005) A vector space search engine for web services. Web Services, 2005. ECOWS 2005. Third IEEE European Conference on pp 9 (2005). doi:10.1109/ECOWS.2005.5

  22. Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994) Grouplens: an open architecture for collaborative filtering of netnews. ACM Press, New York, pp 175–186

    Google Scholar 

  23. Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40(3): 56–58. doi:10.1145/245108.245121

    Article  Google Scholar 

  24. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11): 613–620. doi:10.1145/361219.361220

    Article  MATH  Google Scholar 

  25. Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: WWW ’01: Proceedings of the 10th international conference on world wide web. ACM, New York, NY, USA, pp 285–295. doi:10.1145/371920.372071

  26. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for e-commerce. In: EC ’00: Proceedings of the 2nd ACM conference on electronic commerce. ACM, New York, NY, USA, pp 158–167. doi:10.1145/352871.352887

  27. Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. ACM Press, New York, pp 210–217

    Google Scholar 

  28. Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 4:2–4

    Google Scholar 

  29. Wang Z, Liu K, Lv G, Hao X (2007) Study of an algorithm of web service matching based on semantic web service. In: ALPIT ’07: Proceedings of the sixth international conference on advanced language processing and web information technology (ALPIT 2007). IEEE Computer Society, Washington, DC, USA, pp 429–433

  30. Wu C, Potdar V, Chang E (2009) Advances in web semantics I: ontologies, web services and applied semantic web, chap. Latent semantic analysis—the dynamics of semantics web services discovery. Springer, Berlin, 346–373. doi:10.1007/978-3-540-89784-2_14

    Google Scholar 

  31. Wu CT, Wang HF (2007) Recent development of recommender systems. 2007 IEEE International Conference on Industrial Engineering and Engineering Management. pp 228–232. doi:10.1109/IEEM.2007.4419185

  32. Wu S (2009) A new web services matching algorithm. In: IUCE ’09: Proceedings of the 2009 international symposium on intelligent ubiquitous computing and education. IEEE Computer Society, Washington, DC, USA, pp 414–416

  33. Yu T, Zhang Y, Lin KJ (2007) Efficient algorithms for web services selection with end-to-end qos constraints. ACM Trans Web 1. doi:10.1145/1232722.1232728

  34. Zheng Z, Ma H, Lyu MR, King I (2009) Wsrec: a collaborative filtering based web service recommender system. In: ICWS ’09: Proceedings of the 2009 IEEE international conference on web services. IEEE Computer Society, Washington, DC, USA, pp 437–444

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Authors and Affiliations

  1. Information Department, TELECOM and Management SudParis, UMR 5157 CNRS Samovar, Evry, France

    Nguyen Ngoc Chan, Walid Gaaloul & Samir Tata

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  1. Nguyen Ngoc Chan
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  2. Walid Gaaloul
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  3. Samir Tata
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Correspondence to Nguyen Ngoc Chan.

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Chan, N.N., Gaaloul, W. & Tata, S. A recommender system based on historical usage data for web service discovery. SOCA 6, 51–63 (2012). https://doi.org/10.1007/s11761-011-0099-2

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