Recommender Systems Using Ensemble Techniques

This study proposes a novel recommender system that uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user’s preference. The proposed model consists of two steps. In the first step, this study uses logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. Then, this study combines the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. In the second step, this study uses the market basket analysis to extract association rules for co-purchased products. Finally, the system selects customers who have high likelihood to purchase products in each product group and recommends proper products from same or different product groups to them through above two steps. We test the usability of the proposed system by using prototype and real-world transaction and profile data. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The results also show that the proposed system may be useful in real-world online shopping store.

• Yeonjeong Lee
• Kyoung-jae Kim
• Youngtae Kim

• Product recommender system
• Ensemble technique
• Association rules
• Decision tree
• Artificial neural networks.

[1] M. J. Pazzani, and D. Billsus, “Content-Based Recommendation Systems,”
Lecture Notes in Computer Science, vol. 4321, pp. 325–341, 2007.
[2] K. –L. Wu, C. C. Aggarwal, and P. S. Yu, “Personalization with dynamic
profiler,” in Proceedings of the Third International Workshop on
Advanced Issues of E-commerce and Web-based Information Systems,
2001, pp. 12–20.
[3] J. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive
algorithms for collaborative filtering,” in Proceedings of the 14th
Conference on Uncertainty in Artificial Intelligence, San Francisco, CA,
1998, pp. 43–52.
[4] J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl,
“GroupLens: Applying Collaborative Filtering to Usenet News,”
Communication of the ACM, vol. 40, pp. 77–87, 1997.
[5] M. J. Pazzani, “A framework for collaborative, content-based and
demographic filtering,” Artificial Intelligence Review, vol. 13, no. 5-6, pp.
393–408, 1999.
[6] B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl, “Analysis of
recommendation algorithms for e-commerce,” in Proceedings of
Conference on ACM Electronic Commerce, 2000, pp. 158–167.
[7] J. K. Kim, Y. H. Cho, W. J. Kim, J. R. Kim, and J. H. Suh, “A
personalized recommendation procedure for Internet shopping support,”
Electronic Commerce Research and Applications, vol. 1, pp. 301–313,
2002.
[8] J. K. Kim, J. H. Suh, D. H. Ahn, and Y. H. Cho, “A personalized
recommendation methodology based on collaborative filtering,” Journal
of Intelligence and Information Systems, vol. 8, no. 2, pp. 139–157, 2002.
[9] Y. H. Cho, J. K. Kim, and S. H. Kim, “A personalized recommender
system based on Web usage mining and decision tree induction,” Expert
Systems with Applications, vol. 23, pp. 329–342, 2002.
[10] J. K. Kim, D. H. Ahn, and Y. H. Cho, “Development of a personalized
recommendation procedure based on data mining techniques for internet
shopping malls,” Journal of Intelligence and Information Systems, vol. 9,
no.3, pp. 177–191, 2003.
[11] J. W. Kim, S. J. Bae, and H. J. Lee, “Sparsity Effect on Collaborative
Filtering-based Personalized Recommendation,” Asia Pacific Journal of
Information Systems, vol.14, no.2, pp. 131~149, 2004.
[12] Y. H. Cho, and J. K. Kim, “Application of Web usage mining and product
taxonomy to collaborative recommendations in e-commerce,” Expert
Systems with Applications, vol. 26, pp. 233–246, 2004.
[13] Y. H. Cho, S. K. Park, D. H. Ahn, and J. K. Kim, “Collaborative
Recommendations using Adjusted Product Hierarchy: Methodology and
Evaluation,” Journal of the Korean Operations Research and
Management Science Society, vol. 29, no. 2, pp. 59–75, 2004.
[14] J. K. Kim, D. H. Ahn, and Y. H. Cho, “A Personalized Recommender
System, WebCF-PT: A Collaborative Filtering using Web Mining and
Product Taxonomy,” Asia Pacific Journal of Information Systems, vol. 15,
no. 1, pp. 63–79, 2005.
[15] D. Kim, and B. -J. Yum, “Collaborative filtering based on iterative
principal component analysis,” Expert Systems with Applications, vol. 28,
no. 4, pp. 823~830, 2005.
[16] G. Adomavicius, and A. Tuzhilin, “Toward the next generation of
recommender systems: a survey of the state-of-the-art and possible
extensions,” IEEE Transactions on Knowledge and Data Engineering,
vol. 17, no. 6, pp. 734–749, 2005.
[17] K. Kim, and. B. Kim, “Product Recommender System for Online
Shopping Malls using Data Mining Techniques,” Journal of Intelligence
and Information Systems, vol.11, no.1, pp. 191~205, 2005.
[18] H. C. Ahn, I. Han, and K. Kim, “The Product Recommender System
Combining Association Rules and Classification Models: The Case of G
Internet Shopping Mall,” Information Systems Review, vol.8, no.1, pp.
181~201, 2006.
[19] K. Kim, and H. Ahn, “Collaborative filtering with a user-item matrix
reduction technique for recommender systems,” International Journal of
Electronic Commerce, vol. 16, no. 1, pp. 107–128, 2011.
[20] M. Papagelis, D. Plexousakis, and T. Kutsuras, “Alleviating the sparsity
problem of collaborative filtering using trust inferences,” in iTrust,
Springer-Verlag, 2005, pp. 224–239.
[21] L. Breiman, “Heuristics of instability in model selection,” in Technical
Report, Statistics Department, University of California at Berkeley, 1994.
[22] L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp.
123–140, 1996.
[23] R. Tibshirani, and K. Knight, “Model search and inference by bootstrap
“bumping”,” in Technical Report, University of Toronto, 1995.
[24] T. Heskes, “Balancing between bagging and bumping,” in Advances in
Neural Information Processing Systems, Cambridge, MIT Press, 1996, pp.
466–472.
[25] Y. Lee, and S. Kwak, “A study on training ensembles of neural networks:
a case of stock price prediction,” Journal of Intelligence and Information
Systems, vol. 5, no. 1, pp. 95–101, 1999.