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A Multigraph Approach for Web Services Recommendation

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On the Move to Meaningful Internet Systems: OTM 2016 Conferences (OTM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10033))

Abstract

In this paper, we describe a Web services recommendation approach where the services’ ecosystem is represented as a heterogeneous multigraph, and edges may have different semantics. The recommendation process relies on clustering techniques to suggest services “of interest” to a user. Our approach has been implemented as a tool called WesReG (Web services Recommendation with Graphs) on top of Neo4j and its cypher query language. We present the system implementation details and present the results of experiments on a collection of real Web services.

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Notes

  1. 1.

    http://www.programmableweb.com/.

  2. 2.

    http://linkeddata.org/.

  3. 3.

    http://www.lsis.org/sellamis/Projects.html#WeS-ReG.

  4. 4.

    http://www.librec.net/.

References

  1. Berry, M.W., Drmac, Z., Jessup, E.R.: Matrices, vector spaces, and information retrieval. SIAM Rev. 41, 335–362 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bobadilla, J., Ortega, F., Hernando, A., GutiéRrez, A.: Recommender systems survey. Know. Based Syst. 46, 109–132 (2013)

    Article  Google Scholar 

  3. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)

    Google Scholar 

  4. Chen, W., Paik, I., Hung, P.C.K.: Constructing a global social service network for better quality of web service discovery. IEEE Trans. Serv. Comput. 8, 284–298 (2015)

    Article  Google Scholar 

  5. Chen, Z., Jiang, Y., Zhao, Y.: A collaborative filtering recommendation algorithm based on user interest change and trust evaluation. JDCTA 4, 106–113 (2010)

    Google Scholar 

  6. Choi, K., Yoo, D., Kim, G., Suh, Y.: A hybrid online-product recommendation system: combining implicit rating-based collaborative filtering and sequential pattern analysis. Electron. Commer. Res. Appl. 11, 309–317 (2012)

    Article  Google Scholar 

  7. Deng, S., Huang, L., Yin, Y., Tang, W.: Trust-based service recommendation in social network. Appl. Math. 9, 1567–1574 (2015)

    Google Scholar 

  8. Deng, S., Huang, L., Xu, G.: Social network-based service recommendation with trust enhancement. Expert Syst. Appl. 41, 8075–8084 (2014)

    Article  Google Scholar 

  9. Golbeck, J., Hendler, J.: Filmtrust: movie recommendations using trust in web-based social networks. In: Proceedings of the IEEE Consumer Communications and Networking Conference, vol. 96, pp. 282–286 (2006)

    Google Scholar 

  10. Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 123–129 (2015)

    Google Scholar 

  11. Heitmann, B., Hayes, C.: Using linked data to build open, collaborative recommender systems. In: AAAI Spring Symposium: Linked Data Meets Artificial Intelligence, pp. 76–81 (2010)

    Google Scholar 

  12. Maamar, Z., Wives, L.K., Badr, Y., Elnaffar, S., Boukadi, K., Faci, N.: Linkedws: A novel web services discovery model based on the metaphor of Social networks. Simul. Model. Pract. Theor. 19, 121–132 (2011)

    Article  Google Scholar 

  13. Maaradji, A., Hacid, H., Skraba, R., Lateef, A., Daigremont, J., Crespi, N.: Social-based web services discovery and composition for step-by-step mashup completion. In: IEEE International Conference on Web Services (ICWS 2011), pp. 700–701 (2011)

    Google Scholar 

  14. Mashal, I., Chung, T.-Y., Alsaryrah, O.: Toward service recommendation in internet of things. In: 2015 Seventh International Conference on Ubiquitous and Future Networks (ICUFN), pp. 328–331. IEEE (2015)

    Google Scholar 

  15. Massa, P., Avesani, P.: Trust-aware collaborative filtering for recommender systems. In: Meersman, R. (ed.) CoopIS/DOA/ODBASE 2004. LNCS, vol. 3290, pp. 492–508. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  16. Slaimi, F., Sellami, S., Boucelma, O., Ben Hassine, A.: Flexible matchmaking for restful web services. In: Meersman, R., et al. (eds.) OTM 2013. LNCS, vol. 8185, pp. 542–554. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  17. Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agent. Multi-Agent Syst. 16, 57–74 (2008)

    Article  Google Scholar 

  18. Zhang, X., He, K., Wang, J., Wang, C., Tian, G., Liu, J.: Web service recommendation based on watchlist via temporal and tag preference fusion. In: 2014 IEEE International Conference on Web Services (ICWS), pp. 281–288. IEEE (2014)

    Google Scholar 

  19. Kim, C., Kim, J.: A recommendation algorithm using multi-level association rules. In: IEEE/WIC International Conference on Web Intelligence (WI 2003), pp. 524–527 (2003)

    Google Scholar 

  20. Bianchini, D., De Antonellis, V., Melchiori, M.: Link-based viewing of multiple web API repositories. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, Roland R. (eds.) DEXA 2014, Part I. LNCS, vol. 8644, pp. 362–376. Springer, Heidelberg (2014)

    Google Scholar 

  21. Zheng, Z.B., Ma, H., Lyu, M.R., King, I.: QoS-aware web service recommendation by collaborative filtering. IEEE Trans. Serv. Comput. 4, 140–152 (2011)

    Article  Google Scholar 

  22. Cao, J., Wu, Z., Wang, Y., Zhuang, Y.: Hybrid collaborative filtering algorithm for bidirectional Web service recommendation. Knowl. Inf. Syst. 36(3), 607–627 (2013)

    Google Scholar 

  23. Manikrao, U.S., Prabhakar, T.V.: Dynamic selection of web services with recommendation system. In: International Conference on Next Generation Web Services Practices (NWeSP 2005) (2005)

    Google Scholar 

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Correspondence to Fatma Slaimi .

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Slaimi, F., Sellami, S., Boucelma, O., Ben Hassine, A. (2016). A Multigraph Approach for Web Services Recommendation. In: Debruyne, C., et al. On the Move to Meaningful Internet Systems: OTM 2016 Conferences. OTM 2016. Lecture Notes in Computer Science(), vol 10033. Springer, Cham. https://doi.org/10.1007/978-3-319-48472-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-48472-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48471-6

  • Online ISBN: 978-3-319-48472-3

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