Advertisement

Information Systems Frontiers

, Volume 15, Issue 4, pp 533–551 | Cite as

Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm

  • Chen WeiEmail author
  • Richard Khoury
  • Simon Fong
Article

Abstract

Recommendation Services (RS) are an essential part of online marketing campaigns. They make it possible to automatically suggest advertisements and promotions that fit the interests of individual users. Social networking websites, and the Web 2.0 in general, offer a collaborative online platform where users socialize, interact and discuss topics of interest with each other. These websites have created an abundance of information about users and their interests. The computational challenge however is to analyze and filter this information in order to generate useful recommendations for each user. Collaborative Filtering (CF) is a recommendation service technique that collects information from a user’s preferences and from trusted peer users in order to infer a new targeted suggestion. CF and its variants have been studied extensively in the literature on online recommending, marketing and advertising systems. However, most of the work done was based on Web 1.0, where all the information necessary for the computations is assumed to always be completely available. By contrast, in the distributed environment of Web 2.0, such as in current social networks, the required information may be either incomplete or scattered over different sources. In this paper, we propose the Multi-Collaborative Filtering Trust Network algorithm, an improved version of the CF algorithm designed to work on the Web 2.0 platform. Our simulation experiments show that the new algorithm yields a clear improvement in prediction accuracy compared to the original CF algorithm.

Keywords

Recommendation system Collaborative Filtering Social network Web 2.0 

References

  1. Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American.Google Scholar
  2. Brenner, A., Pradel, B., Usunier, N., & Gallinari, P. (2010). Predicting most rated items in weekly recommendation with temporal regression. Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys), pp. 24–27.Google Scholar
  3. Campos, P. G., Bellogin, A., Diez, F., & Chvarriaga, J. E. (2010). Simple time-biased KNN-based recommendations. Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys), pp. 20–23Google Scholar
  4. Debnath, S., Ganguy, N., & Mitra, P. (2008). Feature weighting in content based recommendation system using social network analysis. 17th International Conference on WWW, 1041–1042.Google Scholar
  5. Desarkar, M. S., Sarkar, S., & Mitra, P. (2010). Aggregating preference graphs for collaborative rating prediction. Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys), 21–28.Google Scholar
  6. Dwyer, C., Hiltz, S. S. R., & Passerini, K. (2007). Trust and privacy concern within social networking sites: a comparison of Facebook and MySpace. Proceedings of the Thirteenth Americas Conference on Information (AMCIS 2007), 339–351.Google Scholar
  7. Facebook Project Research & Resource. http://thefacebookproject.com/resource/datasets.html. Accessed 18 November 2011.
  8. FrontlineSolvers XLMiner. http://www.solver.com/xlminer/. Accessed 18 November 2011.
  9. Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. Proceedings of the 27th International Conference on Human Factors in Computing Systems (CHI 09), 211–220.Google Scholar
  10. Golbeck, J. (2008). Weaving a web of trust. AAAS Science Magazine, 321(5896), 1640–1641.CrossRefGoogle Scholar
  11. GroupLens Research. http://www.grouplens.org/. Accessed 18 November 2011.
  12. Gursel, A., & Sen, S. (2009). Producing timely recommendations from social networks through targeted search. Proceedings of the 8th International Conference on Autonomous Agents and Multi-agent Systems, 2, 805–812.Google Scholar
  13. Hung, L.-P. (2005). A personalized recommendation system based on product taxonomy for one-to-one marketing online. International Journal of Expert Systems with Applications, 29(2), 383–392.CrossRefGoogle Scholar
  14. Kazienko, P., & Musial, K. (2006). Recommendation framework for online social networks. Advances in Web Intelligence and Data Mining, Springer, 23, 111–120.CrossRefGoogle Scholar
  15. Kitisin, S., & Neuman, C. (2006). Reputation-based trust-aware recommender system. Securecomm and Workshops, pp. 1–7.Google Scholar
  16. Korvenmaa, P. (2009). The growth of an Online Social Networking Service Conception of Substantial Elements. Master Thesis, Teknillinen Korkeakoulu, Espoo.Google Scholar
  17. Liu, N. N., Zhao, M., Xiang, E., & Yang, Q. (2010). Online evolutionary collaborative filtering. Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys), pp. 95–102.Google Scholar
  18. Massa, P., & Avesani, P. (2007). Trust-aware recommender systems. Proceedings of the 2007 ACM conference on Recommender systems (RecSys), pp. 17–24.Google Scholar
  19. Massa, P., & Bhattacharjee B. (2004). Using trust in recommender systems: An experimental analysis. Second International Conference in Trust Management (iTrust 2004), Lecture Notes in Computer Science, Springer, 2995, pp. 221–235.Google Scholar
  20. Massari, L. (2010). Analysis of MySpace user profiles. Information Systems Frontiers Special Issue on Ethics and Information Systems, 12(4), 361–367.CrossRefGoogle Scholar
  21. Networking Group Wiki Page. http://odysseas.calit2.uci.edu/doku.php/public:online_social_networks. Accessed 18 November 2011.
  22. Page, L., Brin, S., Motwani, R., & Winograd, T. (1998). The page rank citation ranking: Bringing order to the web. Technical report, Stanford, USA.Google Scholar
  23. Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers.Google Scholar
  24. Rendle, S., & Schmidt-thieme, L. (2010). Factorization models for context-/time-aware movie recommendations encoding time as context. Proceedings of the Workshop on Context Aware Movie Recommendation, pp. 1–6Google Scholar
  25. Sandholm, T., Ung, H., Aperjis, C., & Huberman, B. A. (2010). Global budgets for local recommendations. Proceedings of the 2010 ACM Conference on Recommender Systems (RecSys), pp. 13–20.Google Scholar
  26. Song, J.-G., & Kim, S. (2009). A study on applying context-aware technology on hypothetical shopping advertisement. Information Systems Frontiers Special Issue on Intelligent Systems and Smart Homes, 11(5), 561–567.Google Scholar
  27. Sun, J., Yu, X., Li, X., & Wu, Z. (2008). Research on trust-aware recommender model based on profile similarity. International Symposium on Computational Intelligence and Design (ISCID 08), pp. 154–157.Google Scholar
  28. Sztompka, P. (1999). Trust: A sociological theory. Cambridge: Cambridge University Press.Google Scholar
  29. Vuk, M., & Curk, T. (2006). ROC curve, accumulative gain chart and calibration plot. Metodolo shi zvezki, 3(1), 89–108.Google Scholar
  30. Wei, C., & Fong, S. (2010). Social network collaborative filtering framework and online trust factors: a case study on Facebook. 5th International Conference on Digital Information Management.Google Scholar
  31. Wu, Z., Yu, X., & Sun, J. (2009). An improved trust metric for trust-aware recommender systems. First International Workshop on Education Technology and Computer Science (ETCS 09), pp. 947–951.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Lenovo ChinaBeijingChina
  2. 2.Faculty of Science and TechnologyUniversity of MacauMacauChina
  3. 3.Department of Software EngineeringLakehead UniversityThunder BayCanada

Personalised recommendations