Weitere Artikel dieser Ausgabe durch Wischen aufrufen
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.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American.
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.
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. 20–23
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.
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.
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.
Facebook Project Research & Resource. http://thefacebookproject.com/resource/datasets.html. Accessed 18 November 2011.
FrontlineSolvers XLMiner. http://www.solver.com/xlminer/. Accessed 18 November 2011.
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.
Golbeck, J. (2008). Weaving a web of trust. AAAS Science Magazine, 321(5896), 1640–1641. CrossRef
GroupLens Research. http://www.grouplens.org/. Accessed 18 November 2011.
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.
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. CrossRef
Kazienko, P., & Musial, K. (2006). Recommendation framework for online social networks. Advances in Web Intelligence and Data Mining, Springer, 23, 111–120. CrossRef
Kitisin, S., & Neuman, C. (2006). Reputation-based trust-aware recommender system. Securecomm and Workshops, pp. 1–7.
Korvenmaa, P. (2009). The growth of an Online Social Networking Service Conception of Substantial Elements. Master Thesis, Teknillinen Korkeakoulu, Espoo.
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.
Massa, P., & Avesani, P. (2007). Trust-aware recommender systems. Proceedings of the 2007 ACM conference on Recommender systems (RecSys), pp. 17–24.
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.
Massari, L. (2010). Analysis of MySpace user profiles. Information Systems Frontiers Special Issue on Ethics and Information Systems, 12(4), 361–367. CrossRef
Networking Group Wiki Page. http://odysseas.calit2.uci.edu/doku.php/public:online_social_networks. Accessed 18 November 2011.
Page, L., Brin, S., Motwani, R., & Winograd, T. (1998). The page rank citation ranking: Bringing order to the web. Technical report, Stanford, USA.
Quinlan, J. R. (1993). C4.5: Programs for machine learning. Morgan Kaufmann Publishers.
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. 1–6
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.
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.
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.
Sztompka, P. (1999). Trust: A sociological theory. Cambridge: Cambridge University Press.
Vuk, M., & Curk, T. (2006). ROC curve, accumulative gain chart and calibration plot. Metodolo shi zvezki, 3(1), 89–108.
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.
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.
- Web 2.0 Recommendation service by multi-collaborative filtering trust network algorithm
- Springer US
Neuer Inhalt/© ITandMEDIA