Browse > Article
http://dx.doi.org/10.11627/jkise.2013.36.2.74

Recommender System based on Product Taxonomy and User's Tendency  

Lim, Heonsang (Samsung Electronics)
Kim, Yong Soo (Department of Industrial and Management Engineering, Kyonggi University)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.36, no.2, 2013 , pp. 74-80 More about this Journal
Abstract
In this study, a novel and flexible recommender system was developed, based on product taxonomy and usage patterns of users. The proposed system consists of the following four steps : (i) estimation of the product-preference matrix, (ii) construction of the product-preference matrix, (iii) estimation of the popularity and similarity levels for sought-after products, and (iv) recommendation of a products for the user. The product-preference matrix for each user is estimated through a linear combination of clicks, basket placements, and purchase statuses. Then the preference matrix of a particular genre is constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product with respect to the total. The popularity and similarity levels of a user's clicked product are estimated with an entropy index. Based on this information, collaborative and content-based filtering is used to recommend a product to the user. To assess the effectiveness of the proposed approach, an empirical study was conducted by constructing an experimental e-commerce site. Our results clearly showed that the proposed hybrid method is superior to conventional methods.
Keywords
Recommender System; Collaborative Filtering; Content based Filtering; Product Taxonomy;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Kim, Y.S., Text Recommender System Using User's Usage Patterns, Industrial Management and Data Systems, 2011, Vol. 111, No. 2, p 282-297.   DOI
2 Bose, R., Advanced Analytics : Opportunities and Challenges, Industrial Management and Data Systems, 2009, Vol. 109, No. 2, p 155-172.   DOI
3 Choi, S.H., Jeong, Y.S., and Jeong, M.K., A Hybrid Recommendation Method with Reduced Data for Large- Scale Application, IEEE Transactions on Systems, Man, and Cybernetics-Part C : Applications and Reviews, 2010, Vol. 40, No. 5, p 557-566.   DOI
4 Curtrini, E., Using Entropy Measures to Disentangle Regional from National Localization Patterns, Regional Science and Urban Economics, 2009, Vol. 39, p 243- 250.   DOI
5 Hayes, C., Cunningham, P., and Smyth, B., A Case-Based Reasoning view of Automated Collaborative Filtering, Proceedings of the Fourth International Conference on Case-Based Reasoning, 2001, p 243-248.
6 Mooney, R.J. and Roy, I., Content-based Boork Recommending using Learning for Text Categorization, Proceedings of the 18th National Conference on Artificial Intelligence, 2000, p 187-192.
7 Kim, Y.S., Recommender System based on Product Taxonomy in E-Commerce Sites, Journal of Information Science and Engineering, 2013, Vol. 29, p 63-78.
8 Kim, Y.S., Yum, B.-J., Song, J., and Kim, S.M., Development of a Recommender System based on Navigational and Behavioral Patterns of Customers in E-Commerce Sites. Expert Systems with Applications, 2005, Vol. 28, No. 2, p 381-393.   DOI
9 Lawrence, R.D., Almasi, G.S., Korlyar, V., Viveros, M.S., and Duri, S.S., Personalization of Supermarket Product Recommendations, Data Mining and Knowledge Discovery, 2001, Vol. 5, No. 1, p 67-77.
10 Rennolls, K., Likelihood, Entropy and Species Diversity : Some Comparisons in a Sumatran Forest, Proceedings of Forest Biometry, Modeling and Information Sciences, 2001.
11 Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedle, J., Grouplens : An Open Architecture for Collaborative Filtering of Netnews, Proceedings of the ACM Conference on Computer Supported Cooperative Work, 1994, p 175-186.
12 Sarwar, B., Karypis, G., Konstan, J.A., and Riedle, J., Analysis of Recommendation Algorithms for E-Commerce, Proceedings of the ACM E-Commerce Conference, 2000, p 158-167.
13 Shannon, C.E., A Mathematical Theory of Communication, Bell System Journal, Vol. 27, p 379-42.
14 Shardanand, U. and Maes, P., Social Information Filtering : Algorithms for Automating Word of Mouth, Proceedings of Conference on Human Factors in Computing Systems, 1995, p 210-217.
15 Shyu, M.-L., Chen, S.-C., Chen, M., Zhang, C., and Sarinnapakorn, K., Image Database Retrieval Utilizing Affinity Relationships, Proceedings of the First ACM International Workshop on Multimedia Databases, 1995, p 78-85.
16 Sun, J., Wang, Z., Yu, H., Nihino, F., Katusyama, Y., and Naoi, S., Effective Text Extraction and Recognition for WWW Images, Proceedings of the 2003 ACM Symposium on Document Engineering, 2003, p 115-117
17 Wei, C.-P., Yang,C.-H., and Hsiao, H.-W., A Collaborative Filtering-based Approach to Personalized Document Clustering, Decision Support Systems, 2008, Vol. 45, p 413-428.   DOI