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Collaborative personal profiling for web service ranking and recommendation

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Abstract

Web service is one of the most fundamental technologies in implementing service oriented architecture (SOA) based applications. One essential challenge related to web service is to find suitable candidates with regard to web service consumer’s requests, which is normally called web service discovery. During a web service discovery protocol, it is expected that the consumer will find it hard to distinguish which ones are more suitable in the retrieval set, thereby making selection of web services a critical task. In this paper, inspired by the idea that the service composition pattern is significant hint for service selection, a personal profiling mechanism is proposed to improve ranking and recommendation performance. Since service selection is highly dependent on the composition process, personal knowledge is accumulated from previous service composition process and shared via collaborative filtering where a set of users with similar interest will be firstly identified. Afterwards a web service re-ranking mechanism is employed for personalised recommendation. Experimental studies are conduced and analysed to demonstrate the promising potential of this research.

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References

  • Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734–749. doi:10.1109/TKDE.2005.99.

    Article  Google Scholar 

  • Aggarwal, C.C., & Yu, P.S. (1998). A new framework for itemset generation. In Proceedings of 17th ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (pp. 18–24). doi:10.1145/275487.275490, db/conf/pods/AggarwalY98.html.

  • Agrawal, R., Imielinski, T., Swami, A.N. (1993). Mining association rules between sets of items in large databases. In Proceedings of 1993 ACM SIGMOD international conference on management of data (pp. 207–216). doi:10.1145/170035.170072, db/conf/sigmod/AgrawalIS93.html..

  • Akkiraju, R., Farrell, J., Miller, J., Nagarajan, M., Schmidt, M.-T., Sheth, A., Verma, K. (2005). Web service semantics—WSDL-S. http://www.w3.org/Submission/WSDL-S/.

  • Al-Masri, E., & Mahmoud, Q.H. (2008). Investigating Web services on the World Wide Web. In Proceedings of 17th international conference on World Wide Web (pp. 795–804). doi:10.1145/1367497.1367605.

  • Barros, A., Dumas, M., Bruza, P. (2005). The move to Web service ecosystems. BPTrends Newsletter, 3(12).

  • Basu, S., Casati, F., Daniel, F. (2007). Web service dependency discovery tool for SOA management. In Proceedings of 4th IEEE international conference on services computing (pp. 684–685). doi:10.1109/SCC.2007.130.

  • Breese, J.S., Heckerman, D., Kadie, C.M. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of 14th international conference on uncertainty in artificial intelligence (pp. 43–52). http://rome.exp.sis.pitt.edu/UAI/Abstract.asp?articleID=231&proceedingID=14.

  • Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S. (1999). Web caching and Zipf-like distributions: evidence and implications. In Proceedings of 18th annual joint conference of the IEEE computer and communications societies (pp. 126–134).

  • Brin, S., Motwani, R., Ullman, J.D., Tsur, S. (1997). Dynamic itemset counting and implication rules for market basket data. In Proceedings of 1994 ACM SIGMOD international conference on management of data (pp. 255–264). doi:10.1145/253260.253325, db/conf/sigmod/BrinMUT97.html.

  • Burstein, M.H., Hobbs, J.R., Lassila, O., Martin, D.L., McDermott, D.V., McIlraith, S.A., Narayanan, S., Paolucci, M., Payne, T.R., Sycara, K.P. (2002). DAML-S: Web service description for the semantic Web. In Proceedings of 1st international semantic web conference (pp. 348–363). http://link.springer.de/link/service/series/0558/bibs/2342/23420348.htm.

  • Cervantes, H., & Hall, R.S. (2003). Automating sService dependency management in a service-oriented component model. In Proceedings of 6th international workshop on component-based software engineering.

  • Chen, W., & Paik, I. (2013). Improving efficiency of service discovery using Linked data-based service publication. Information Systems Frontiers, 15(4), 613–625. doi:10.1007/s10796-012-9381-x.

    Article  Google Scholar 

  • Chen, X., Liu, X., Huang, Z., Sun, H. (2010). RegionKNN: a scalable hybrid collaborative filtering algorithm for personalized Web service recommendation. In Proceedings of 8th IEEE international conference on web services (pp. 9–16). doi:10.1109/ICWS.2010.27.

  • Claypool, M., Le, P., Waseda, M., Brown, D. (2001). Implicit interest indicators. In Proceedings of 2001 international conference on intelligent user interfaces (pp. 33–40). doi:10.1145/359784.359836.

  • Corella, M.Á., & Castells, P. (2006). Semi-automatic semantic-based Web service classification. In Proceedings of 2001 international conference on intelligent user interfaces (pp. 459–470). doi:10.1007/11837862_43.

  • Cormack, G.V., & Lynam, T.R. (2006). Statistical precision of information retrieval evaluation. In Proceedings of 29th annual international conference on research and development in information retrieval (pp. 533–540). doi:10.1145/1148170.1148262.

  • de Bruijn, J., Lausen, H., Polleres, A., Fensel, D. (2006). The Web service modeling language WSML: an overview. In Proceedings of 3rd European semantic Web conference (pp. 590–604). doi:10.1007/11762256_43.

  • Fang, J., Hu, S., Han, Y. (2004). A service interoperability assessment model for service composition. In Proceedings of 2004 IEEE international conference on services computing (pp. 153–158). http://csdl.computer.org/comp/proceedings/scc/2004/2225/00/22250153abs.htm.

  • Feng, Z., Peng, R., Wong, R.K., He, K., Wang, J., Hu, S., Li, B. (2013). QoS-aware and multi-granularity service composition. Information Systems Frontiers, 15(4), 553–567. doi:10.1007/s10796-012-9378-5.

    Article  Google Scholar 

  • Fonseca, B.M., Golgher, P.B., Pôssas, B., Ribeiro-Neto, B.A., Ziviani, N. (2005). Concept-based interactive query expansion. In Proceedings of 2005 ACM international conference on information and knowledge management (pp. 696–703). doi:10.1145/1099554.1099726.

  • Gu, Q., & Lago, P. (2009). Exploring service-oriented system engineering challenges: a systematic literature review. Service Oriented Computing and Applications, 3(3), 171–188. doi:10.1007/s11761-009-0046-7.

    Article  Google Scholar 

  • Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of 22nd annual international ACM SIGIR conferenceon research and development in information retrieval (230–237). doi:10.1145/312624.312682.

  • Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5–53. doi:10.1145/963770.963772.

    Article  Google Scholar 

  • Huang, Y.S., & Suen, C.Y. (1995). A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 17(1), 90–94. http://www.computer.org/tpami/tp1995/i0090abs.htm.

    Article  Google Scholar 

  • Huang, Z., Chen, H., Zeng, D.D. (2004). Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22(1), 116–142. doi:10.1145/963770.963775.

    Article  Google Scholar 

  • Huang, Z., Zeng, D., Chen, H. (2007). A comparison of collaborativefiltering recommendation algorithms for E-commerce. IEEE Intelligent Systems, 22(5), 68–78. doi:10.1109/MIS.2007.80.

    Article  Google Scholar 

  • Kerrigan, M. (2006). Web service selection mechanisms in the Web service execution environment (WSMX). In Proceedings of 2006 ACM symposium on applied computing (pp. 1664–1668). doi:10.1145/1141277.1141671.

  • Klusch, M., Fries, B., Sycara, K.P. (2006). Automated semantic Web service discovery with OWLS-MX. In Proceedings of 5th international joint conference on autonomous agents and multiagent systems (pp. 915–922). doi:10.1145/1160633.1160796.

  • Kokash, N., Birukou, A., D’Andrea, V. (2007). Web service discovery based on past user experience. In Proceedings of 10th international conference on business information systems (pp. 95–107). doi:10.1007/978-3-540-72035-5_8.

  • Kuck, J., & Gnasa, M. (2007). Context-sensitive service discovery meets information retrieval. In Proceedings of 5th IEEE international conference on pervasive computing and communications (pp. 601–605). doi:10.1109/PERCOMW.2007.32.

  • Li, Y., Liu, Y., Zhang, L.-J., Li, G., Xie, B., Sun, J. (2007). An exploratory study of Web services on the internet. In Proceedings of 5th IEEE international conference on Web services (pp. 380–387). doi:10.1109/ICWS.2007.37.

  • Li, X., Guo, L., Zhao, Y.E. (2008). Tag-based social interest discovery. In Proceedings of 17th international conference on World Wide Web (pp. 675–684). doi:10.1145/1367497.1367589.

  • Liang, Q.A., Chung, J.-Y., Miller, S., Yang, O. (2006). Service pattern discovery of Web service mining in Web service registry-repository. In Proceedings of 2006 IEEE international conference on e-business engineering (pp. 286–293). doi:10.1109/ICEBE.2006.90.

  • Maamar, Z., Mostéfaoui, S.K., Mahmoud, Q.H. (2005). Context for personalized Web services. In Proceedings of 38th Hawaii international conference on system sciences. doi:10.1109/HICSS.2005.164.

  • Manikrao, U.S., & Prabhakar, T.V. (2005). Dynamic selection of Web services with recommendation system. In Proceedings of 2005 international conference on next generation web services practices (pp. 117–121). doi:http://dx.doi.org/10.1109/NWESP.2005.32.

  • Martin, D.L., Paolucci, M., McIlraith, S.A., Burstein, M.H., McDermott, D.V., McGuinness, D.L., Parsia, B., Payne, T.R., Sabou, M., Solanki, M., Srinivasan, N., Sycara, K.P. (2004). Bringing semantics to Web services: the OWL-S approach. In Proceedings of 1st international workshop on semantic web services and web process composition (pp. 26–42). http://dx.doi.org/10.1109/NWESP.2005.32.

  • McLaughlin, M.R., & Herlocker, J.L. (2004). A collaborative filtering algorithm and evaluation metric that accurately model the user experience. In Proceedings of 27th annual international ACM SIGIR conference on research and development in information retrieval (pp. 329–336). doi:10.1145/1008992.1009050.

  • Medjahed, B., & Atif, Y. (2007). Context-based matching forWeb service composition. Distributed and Parallel Databases, 21(1), 5–37. doi:10.1007/s10619-006-7003-7.

    Article  Google Scholar 

  • Moore, J., hong Han, E.-h., Boley, D., Gini, M., Gross, R., Hastings, K., Karypis, G., Kumar, V., Mobasher, B. (1997). Web page categorization and feature selection using association rule and principal component clustering. In Proceedings of 7th workshop on information technologies and systems.

  • Oard, D., & Kim, J. (1998). Implicit feedback for recommender systems. In Proceedings of AAAI workshop on recommender systems (pp. 81–83).

  • Omiecinski, E. (2003). Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering, 15(1), 57–69. http://computer.org/tkde/tk2003/k0057abs.htm.

    Article  Google Scholar 

  • Pires, P.F., Benevides, M.R.F., Mattoso, M. (2002). Building reliable Web services compositions. In Proceedings of NODe 2002 Web and database-related workshops on Web, Web-services, and database systems (pp. 59–72). http://link.springer.de/link/service/series/0558/bibs/2593/25930059.htm.

  • Plasse, M., Niang, N., Saporta, G., Villeminot, A., Leblond, L. (2007). Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set. Computational Statistics & Data Analysis, 52(1), 596–613. doi:10.1016/j.csda.2007.02.020.

    Article  Google Scholar 

  • Rao, J., & Su, X. (2004). A survey of automated Web service composition methods. In Proceedings of 1st international workshop semantic web services and web process composition (pp. 43–54). doi:10.1007/978-3-540-30581-1_5.

  • Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J. (1994). GroupLens: an open architecture for collaborative filtering of netnews. In Proceedings of 1994 ACM conference on computer supported cooperative work (pp. 175–186). doi:10.1145/192844.192905.

  • Rich, E. (1979). User modeling via stereotypes. Cognitive Science, 3(4), 329–354.

    Article  Google Scholar 

  • Rocco, D., Caverlee, J., Liu, L., Critchlow, T. (2005). Domain-specific Web service discovery with service class descriptions. In Proceedings of 3rd IEEE international conference on Web services (pp. 481–488). doi:10.1109/ICWS.2005.49.

  • Rong, W., Liu, K., Liang, L. (2008). Association rule based context modeling for Web service discovery. In Proceedings of 5th IEEE international conference on enterprise computing, Ecommerce and E-services (pp. 299–304). doi:10.1109/CECandEEE.2008.137.

  • Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M. (2002). Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval (pp. 253–260). doi:10.1145/564376.564421.

  • Schoop, M., de Moor, A., Dietz, J.L.G. (2006). The pragmatic Web: a manifesto. Communication of the ACM, 49(5), 75–76. doi:10.1145/1125979.

    Article  Google Scholar 

  • Shani, G., Heckerman, D., Brafman, R.I. (2005). An MDP-based recommender system. Journal of Machine Learning Research, 6, 1265–1295. http://www.jmlr.org/papers/v6/shani05a.html.

    Google Scholar 

  • Shao, L., Zhang, J.,Wei, Y., Zhao, J., Xie, B., Mei, H. (2007). Personalized QoS prediction for Web services via collaborative filtering. In Proceedings of 5th IEEE international conference on Web services (pp. 439–446). doi:10.1109/ICWS.2007.140.

  • Sheng, Q.Z., Benatallah, B., Maamar, Z., Dumas, M., Ngu, A.H.H. (2004). Enabling personalized composition and adaptive provisioning of Web services. In Proceedings of 16th international conference on advanced information systems engineering (pp. 322–337). http://springerlink.metapress.com/openurl.asp?genre=article&issn=0302-9743&volume=3084&spage=322.

  • Skoutas, D., Alrifai, M., Nejdl, W. (2010). Re-ranking Web service search results under diverse user preferences. In Proceedings of 4th international workshop on personalized access, profile management, and context awareness in databases.

  • Sreenath, R.M., & Singh, M.P. (2004). Agent-based service selection. Journal of Web Semantics, 1(3), 261–279. doi:10.1016/j.websem.2003.11.006.

    Article  Google Scholar 

  • Tamani, E., & Evripidou, P. (2007). A pragmatic methodology to Web service discovery. In Proceedings of 5th IEEE international conference on Web services (pp. 1168–1171). doi:10.1109/ICWS.2007.13.

  • Tang, R., & Zou, Y. (2010). An approach for mining Web service composition patterns from execution logs. In Proceedings of 8th IEEE international conference on web services (pp. 678–679). doi:10.1109/ICWS.2010.35.

  • Teevan, J., Dumais, S.T., Horvitz, E. (2005). Personalizing search via automated analysis of interests and activities. In Proceedings of 28th annual international acm sigir conference on research and development in information retrieval (pp. 449–456). doi:10.1145/1076034.1076111.

  • Verheecke, B., Cibrán, M.A., Vanderperren, W., Suvée, D., Jonckers, V. (2004). AOP for dynamic configuration and management of Web services. International Journal of Web Services Research, 1(3), 25–41.

    Article  Google Scholar 

  • Verma, K., Akkiraju, R., Goodwin, R., Doshi, P., Lee, J. (2004). On accommodating inter service dependencies in Web process flow composition. In Proceedings of 2004 AAAI spring symposium (pp. 37–43).

  • Yang, W.-S., Dia, J.-B., Cheng, H.-C., Lin, H.-T. (2006). Mining social networks for targeted advertising. In Proceedings of 39th Hawaii international international conference on systems science. doi:10.1109/HICSS.2006.272.

  • Zheng, Z., Ma, H., Lyu, M.R., King, I. (2009). WSRec: a collaborative filtering based Web service recommender system. In Proceedings of 7th IEEE international conference on Web services (pp. 437–444). doi:10.1109/ICWS.2009.30.

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Acknowledgments

This work was partially supported by the State Key Laboratory of Software Development Environment of China (No. SKLSDE-2013ZX-25), the National Natural Science Foundation of China (No. 61103095), and the National High Technology Research and Development Program of China (No. 2013AA01A601). We are grateful to Shenzhen Ke y Laboratory of Data Vitalization (Smart City) for supporting this research.

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Authors and Affiliations

  1. State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China

    Wenge Rong, Yuanxin Ouyang & Zhang Xiong

  2. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Wenge Rong, Baolin Peng, Yuanxin Ouyang & Zhang Xiong

  3. Research Institute of Beihang University in Shenzhen, Shenzhen, 518057, China

    Wenge Rong, Baolin Peng, Yuanxin Ouyang & Zhang Xiong

  4. Informatics Research Centre, University of Reading, Reading, RG6 6UD, UK

    Kecheng Liu

  5. School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Kecheng Liu

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  1. Wenge Rong
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  3. Yuanxin Ouyang
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Rong, W., Peng, B., Ouyang, Y. et al. Collaborative personal profiling for web service ranking and recommendation. Inf Syst Front 17, 1265–1282 (2015). https://doi.org/10.1007/s10796-014-9495-4

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