Browse > Article
http://dx.doi.org/10.13088/jiis.2011.17.3.079

Enhanced Recommendation Algorithm using Semantic Collaborative Filtering: E-commerce Portal  

Ahmed, Shohel (Computer Science and Information Engineering, Inha University)
Kim, Jong-Woo (Computer Science and Information Engineering, Inha University)
Kang, Sang-Gil (Computer Science and Information Engineering, Inha University)
Publication Information
Journal of Intelligence and Information Systems / v.17, no.3, 2011 , pp. 79-98 More about this Journal
Abstract
This paper proposes a semantic recommendation technique for a personalized e-commerce portal. Semantic recommendation is achieved by utilizing the attributes of products. The semantic similarity of the products is merged with the rating information of the products to provide an accurate recommendation. The recommendation technique also analyzes various attitudes of the customer to evaluate the implicit rating of products. Attitudes are classifies into three types such as "purchasing product", "adding product to shopping cart", and "viewing the product information." We implicitly track customer attitude to estimate the rating of products for recommending products. Also we implement a session validation process to identify the valid sessions that are highly important for giving an accurate recommendation. Our recommendation technique shows a high degree of accuracy as we use age groupings of customers with similar preferences. The experimental section shows that our proposed recommendation method outperforms well known collaborative filtering methods not only for the existing customer, but also for the new user with no previous purchase record.
Keywords
협업필터링;시맨틱유사성;추천방법;전자상거래;개인화;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, "Application of Dimensionality Reduction in Recommender Systems-A Case Study", In Proceedings of the WebKDD 2000 Workshop at the ACM-SIGKDD Conference on Knowledge Discovery in Databases (KDD), Boston, MA, 2000.
2 Deshpande, M. and G. Karypis, "Item-based top-n recommendation algorithms", ACM Transactions on Information Systems, Vol.22, No.1(2004), 143-177.   DOI   ScienceOn
3 Goldberg, D., D. Nichols, B. Oki, and D. Terry, "Using collaborative filtering to weave an information tapestry", Communications of the ACM, Vol. 35, No.12(1992), 61-70.   DOI
4 Shenghua, B., X. Guirong, W. Xiaoyuan, Y. Yong, F. Ben, and S. Zhong, "Optimizing Web Search using Social Annotations", In Proceedings of the 16th international conference on World Wide Web, Banff, Alberta, Canada, (2007), 5012-1510.
5 Sheskin, D., "Handbook of Parametric and Nonparametric Statistical Procedures", Chapman and Hall/CRC, Boca Raton, Florida, 2004.
6 Resnick, P., N. Iacovou, M. Suchak, P. Bergstorm, and J. Riedl, "GroupLens : An Open Architecture for Collaborative Filtering of Netnews", In Proceedings of the Conference on Computer Supported Cooperative Work, Chapel Hill, USA, (1994), 175-186.
7 Aarts, R. M., R. Irwan, and A. J. E. M. Janssen, "Efficient tracking of the cross-correlation coefficient", IEEE transaction on Speech and Audio Processing, Vol.10, No.6(2002), 391-402.   DOI   ScienceOn
8 Aggarwal, C. C., J. L. Wolf, and P. S. Yu, "A New Method for Similarity Indexing for Market Basket Data", In Proceedings of the ACM SIGMOD, Philadelphia, PA, USA, (1999), 407-418.
9 Basu, C., H. Hirsh, and W. Cohen, "Recommendation as Classification : Using Social and Content-based Information in Recommendation", In Proceedings of the 15th National Conference on Artificial Intelligence (AAAI), Madison, WI, 1998.
10 Billsus, D. and M. J. Pazzani, "Learning Collaborative Information Filters", In Proceeding of 15th Int'l Conference of Machine Learning, Madison, USA, (1998), 46-54.
11 Li, M., B. Dias, W. El-Deredy, and P. J. G. Lisboa, "A probabilistic Model for item-based recommender systems", In Proceedings of the Conference of Recommender Systems, Minneapolis, USA, (2007), 129-132.
12 Sarwar, B. M., G. Karypis, J. A. Konstan, and J. T. Riedl, "Analysis of recommendation algorithms for E-commerce", In Proceedings of the 2nd ACM Conference on Electronic Commerce, New York, USA, (2000), 158-167.
13 Sarwar, B., G. Karypis, J. Konstan, and J. Reidl, "Item-based Collaborative Filtering Recommendation Algorithms", In Proceedings of the 10th Int'l Conference on World Wide Web (WWW). Hong Kong, (2001), 285-295.
14 Panagiotis, S., N. Alexandros, P. Apostolos, and M. Yannis, "Collaborative Filtering Based on User Trends", Advances in Data Analysis, Springer Berlin Heidelberg, (2007), 375-382.
15 Resnick, P. and H. Varian, "Recommender Systems", Communications of the ACM, Vol.40, No.3(1997), 56-58.   DOI   ScienceOn
16 McLachlan, G. J., "Mahalanobis distance", Resonance, Springer India, Vol.4, No.6(1999), 20-26.
17 McLauglin R. and J. Herlocher, "A collaborative filtering algorithm and evaluation metric that accurately model the user experience", In Proceedings of the 27th annual international ACM SIGIR, Sheffield, UK, (2004), 329-336.
18 Mobasher, B., X. Jin, and Y. Zhou, "Semantically Enhanced Collaborative Filtering on the Web", Web Mining : From Web to Semantic Web, Lecture Notes in Artificial Intelligence, Vol.3209, Springer, Berlin, Germany, 2004.
19 Liu, J. and G. Deng, "A New-User Cold-Starting Recommendation Algorithm Based on Normalization of Preference", In Proceedings of 4th Int'l conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Dalian, China, (2008), 1-4.
20 Li, Y., L. Lu, and L. Xuefeng, "A Hybrid Collaborative Filtering Method for Multipleinterests and Multiple-content Recommendation in E-Commerce", Expert Systems with Applications, Vol.28, (2005), 67-77.   DOI   ScienceOn
21 Massa, P. and B. Bhattacharjee, "Using Trust in Recommender Systems : An Experimental Analysis", Lecture Notes in Computer Science, Springer-Verlag, Vol.2995(2006), 221-235.
22 Ziegler, C. N., S. M. Mcnee, J. A. Konstan, and G. Lausen, "Improving Recommendation Lists Through Topic Diversification", In Proceedings of 14th Int'l Conference on World Wide Web(WWW), Chiba, Japan, (2005), 22-32.
23 Kang, S., W. Park, and Y. Kim, "Dynamical E-Commerce System for Shopping Mall Site Through Mobile Devices", In Proceedings of 2nd int'l workshop on Data Engineering Issues in E-Commerce and Services (DEECS), San Francisco, USA, (2006), 268-277.
24 Karypis, G., "Evaluation of item-based top-N Recommendation Algorithms", In Proceedings of the 10th ACM Int'l Conference on Information and Knowledge Management (CIKM), Atlanta, GA, (2001), 247-254.
25 Kowalski, G., "Information Retrieval Systems : Theory and implementation", Kluwer Academic publisher, 1997.
26 Huang, Z., D. Zeng, and H. Chen, "A comparative study of recommendation algorithms for e-commerce applications", IEEE Intelligent Systems, forthcoming, 2007.
27 Yang, Y. and X. Liu, "A re-examination of text categorization methods", In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval(SIGIR), Berkeley, CA, (1999), 42-49.
28 Zhang, T. and V. S. Iyengar, "Recommender Systems Using Linear Classifiers", Journal of Machine Learning Research, Vol.2(2002), 313-334.
29 Herlocker, J. L., J. A. Konstan, A. Borchers, and J. Riedl, "An algorithmic framework for performing collaborative filtering", In Proceedings of the 22nd Annual International ACM SIGIR, Berkeley, USA, (1999), 230-237.
30 Herlocker, J. L., J. A. Konstan, L. G. Terveen, and J. T. Riedl, "Evaluating collaborative Filtering Recommender Systems", ACM Transaction on Information Systems, Vol.22, No.1 (2004), 5-53.   DOI   ScienceOn
31 W3C. World Wide Web committee web usage characterization activity. W3C working Draft : Web Characterization Terminology and Definitions Sheet, Pages www.w3.org/1999/05/WCA-terms/.
32 Breese, J. S., D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative Filtering", In Proceedings of the 14th Conference of Uncertainty in Artificial Intelligence, Madison, USA, (1998), 43-52.
33 Hofmann, T., "Latent Semantic Models for Collaborative Filtering", ACM Transactions on Information Systems, Vol.22, No.1(2004), 89-115.   DOI   ScienceOn
34 Heckerman, D., D. M. Chickering, C. Meek, R. Rounthwaite, and C. Kadie, "Dependency Networks for Inference, Collaborative Filtering, and Data Visualization", Journal of Machine Learning Research, Vol.1(2000), 49-75.
35 Wang, G. H., W. S. Wang, T. B. Yu, and Y Gu, "Using Communication Technology for Automation Negotiation in E-commerce Environment", In Proceedings of 2nd Int'l conference on Wireless Communications, Networking and Mobile Computing(WiCOM), Wuhan, China, (2006), 35172-13520.