Improving Recommender Systems in E-Commerce Using Similar Goods

Majid Khalaji, Keramat Mansouri, S. Javad Mirabedini

Department of Computer Engineering, Amirkabir Polytechnic University, Tehran, Iran.
Department of Computer Engineering, Islamic Azad University, Arak Branch, Arak, Iran.
Department of Computer Engineering, Islamic Azad University, Central Tehran Branch, Tehran, Iran..

DOI: 10.4236/jsea.2012.52015                

Abstract

Due to developments of information technology, most of companies and E-shops are looking for selling their products by the Web. These companies increasingly try to sell products and promote their selling strategies by personalization. In this paper, we try to design a Recommender System using association of complementary and similarity among goods and commodities and offer the best goods based on personal needs and interests. We will use ontology that can calculate the degree of complementary, the set of complementary products and the similarity, and then offer them to users. In this paper, we identify two algorithms, CSPAPT and CSPOPT. They have offered better results in comparison with the algorithm of rules; also they don’t have cool start and scalable problems in Recommender Systems.

Keywords

Recommender System; Ontology; Similarity; Complementary; Association Rule; Collaborative Filtering

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	X. H. Sun, F. S. Kong, and S. Ye, “A Comparison of Several Algorithms for Collaborative Filtering in Startup Stage,” Proceedings of IEEE Networking, Sensing and Control, Rome, 19-22 March 2005.
[2]	D. Kalles, A. Papagelis and C. Zaroliagis, “Algorithmic Aspects of Web Intelligent Systems,” WebIntelligence, Springer, Berlin, 2003, pp. 323-345.
[3]	I. Cantador, et al., “A Collaborative Recommendation Framework for Ontology Evaluation and Reuse,” Proceedings of the International Workshop on Recommender Systems, 2006, pp. 67-71.
[4]	N. Belkin and B. Croft, “Information Filtering and Information Retrieval,” ACM, Vol. 35, No. 12, 1999, pp. 29-37. doi:10.1145/138859.138861
[5]	G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems,” A Survey of the State-of-the-Art and Possible Extensions, IEEE, Vol. 17, No. 6, 2005, pp. 734-749.
[6]	M. Papagelisa and D. Plexousakis, “Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents,” Engineering Applications of Artificial Intelligence, Vol. 18, No. 7, 2005, pp. 781-789. doi:10.1016/j.engappai.2005.06.010
[7]	L. Kerschberg, W. Kim, et al., “A Semantic Taxonomy-Based Personalizable Meta-Search Agent,” Journal of Innovative Concepts for Agent-Based Systems, Vol. LNAI 2564, 2003, pp. 3-31.
[8]	R. Knappe, “Measures of Semantic Similarity and Relatedness for Use in Ontology-based Information Retrieval,” Thesis of Doctor, Roskilde University, Roskilde, 2005.
[9]	“OWL Web Ontology Language Reference, W3C Recommendation,” 2004. http://www.w3.org/TR/owl-ref/
[10]	S. H. Moosavi, M. Nematbakhsh and H. K. Farsani, “A Semantic Complement to Enhance Electronic Market,” Expert System with Application, Vol. 40, No. 1, 2009, pp. 1-9.
[11]	G. Kowalski, “Information Retrieval Systems: Theory Andimplementation,” Kluwer Academic Publisher, Dordrecht, 1997.
[12]	G. I. Webb, “Preliminary Investigations into Statistically Valid Exploratory Rule Discovery,” Proceedings of the Australasian Data mining Workshop AUSDM03, University of Technology, Sydney, 2003, pp. 1-9.