Abstract
Conventional collaborative recommendation approaches neglect weak relationships even when they provide important information. This study applies the concepts of chance discovery and small worlds to recommendation systems. The trust (direct or indirect) relationships and product relationships among customers are to find candidates for collaboration. The purchasing quantities and feedback of customers are considered. The whole similarities are calculated based on the model, brand and type of purchased product.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Balabanovic, M., Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM. 40(3), 66–72 (1997)
Basu, C., Hirsh, H., Cohen, W.: Recommendation as Classification: Using Social and Content-Based Information in Recommendation. In: Proceedings of AAAI Symposium on Machine Learning in Information Access (1998)
Battiston, B., Walter, F.E., Schweitzer, F.: Impact of Trust on the Performance of a Recommendation System in a Social Network. In: W Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS’06). Hakodate, Japan (2006)
Goldberg, D., Nichols, D., Oki, B., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM. 35(12), 61–70 (1992)
Krulwich, B., Burkey, C.: Learning User Information Interests through Extraction of Semantically Significant Phrases. In: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access (1996)
Lang, K.: Newsweeder: Learning to Filter Nnetnews. In: Proceedings of the 12th International Conference on Machine Learning, pp. 331–339. Morgan Kaufmann, San Francisco (1995)
Massa, P., Bhattacharjee, B.: Using Trust in Recommender Systems: An Experimental Analysis. In: Jensen, C., Poslad, S., Dimitrakos, T. (eds.) iTrust 2004. LNCS, vol. 2995, pp. 221–235. Springer, Heidelberg (2004)
Montaner, M., Lopez, B., de la Rosa, J.L.: Developing Trust in Recommender Agents. In: Falcone, R., Barber, S., Korba, L., Singh, M.P. (eds.) AAMAS 2002. LNCS (LNAI), vol. 2631, pp. 304–305. Springer, Heidelberg (2002)
Ohsawa, Y., McBurney, P.: Chance Discovery, Advanced Information Processing. Springer, Heidelberg (2003)
Pitsilis, G., Marshall, L.: Trust as a Key to Improving Recommendation System. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 210–223. Springer, Heidelberg (2005)
Pitsilis, G., Marshall, L.: A Proposal for Trust-enabled P2P Recommendation Systems. Technical Report Series (CS-TR-910). University of Newcastle upon Tyne (2005)
O’Donovan, J., Smyth, B.: Trust in Recommender Systems. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 167–174 (2005)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM Press, New York (1994)
Resnick, P., Varian, H.: Recommender Systems. Communications of the ACM 40(3), 56–58 (1997)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings International WWW Conference, pp. 285–295 (2001)
Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ’Word of Mouth’. In: Proceedings of the Conference on Human Factors in Computing Systems (CHI95), pp. 210–217 (1995)
Watts, D.J.: Six Degrees: The Science of a Connected Age. W.W. Norton & Company, New York (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Yang, HL., Yang, HF. (2007). Recommender Agent Based on Social Network. In: Okuno, H.G., Ali, M. (eds) New Trends in Applied Artificial Intelligence. IEA/AIE 2007. Lecture Notes in Computer Science(), vol 4570. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73325-6_94
Download citation
DOI: https://doi.org/10.1007/978-3-540-73325-6_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73322-5
Online ISBN: 978-3-540-73325-6
eBook Packages: Computer ScienceComputer Science (R0)