Abstract
Knowledge-based recommendation has proven to be useful approach for product recommendation where individual products are described in terms of well defined set of features. However existing knowledge-based recommendation systems lack on some issues: it does not allow customers to set importance to the feature with matching options and, it is not possible to set cutoff at individual feature as well as case level at runtime during product recommendation process. In this paper, we have presented a hybrid approach, which integrates rule based reasoning (RBR) and case based reasoning (CBR) techniques to address these issues. We have also described how case based reasoning can be used for clustering i.e. identifying products, which are similar to each other in the product catalog. This is useful when user likes a particular product and wants to see other similar products.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aamodt, A., Plaza, E.: Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications 7(1), 39–52 (1994)
Ricci, F., Nguyen, Q.N.: Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System. IEEE Intelligent systems 22(3), 22–29 (2007)
Linden, G., Smith, B., York, J.: Amazon.com Recommendations Item-to-Item Collaborative Filtering. IEEE Internet Computing, 76–80 (January-February 2003)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Communications of the ACM 40(3), 77–87 (1997)
Resnick, P., Varian, H.R.: Recommander Systems. Communications of the ACM 40(3), 56–58 (1997)
Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)
Lawrence, R.D., Almasi, G.S., Kotlyar, V., Viveros, M.S., Duri, S.S.: Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery 5(1-2), 11–32 (2001)
Sonar, R.M.: An Enterprise Intelligent System Development and Solution Framework. International journal of applied science, engineering and technology 4(1), 1307–4318 (2007)
Burke, R.: Knowledge-based Recommender Systems. In: Kent, A. (ed.) Encyclopedia of Library and Information Systems, New York, vol. 69(32) (2000)
Micarelli, A., Gasparetti, F., Sciarrone, F., Gauch, S.: Personalized search on the world wide web. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 195–230. Springer, Heidelberg (2007)
Felfernig, A., Gula, B., Leitner, G., Maier, M., Melcher, R., Teppan, E.: Persuasion in knowledge-based recommendation. In: Oinas-Kukkonen, H., Hasle, P., Harjumaa, M., Segerståhl, K., Øhrstrøm, P. (eds.) PERSUASIVE 2008. LNCS, vol. 5033, pp. 71–82. Springer, Heidelberg (2008)
Stahl, A.: Combining case-based and similarity-based product recommendation. In: Roth-Berghofer, T.R., Göker, M.H., Güvenir, H.A. (eds.) ECCBR 2006. LNCS (LNAI), vol. 4106, pp. 355–369. Springer, Heidelberg (2006)
Smyth, B.: Case-based recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)
Lim, M., Kim, J.: An adaptive recommendation system with a coordinator agent. In: Zhong, N., Yao, Y., Ohsuga, S., Liu, J. (eds.) WI 2001. LNCS, vol. 2198, pp. 438–442. Springer, Heidelberg (2001)
Burke, R.: Hybrid recommender systems with case-based components. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 91–105. Springer, Heidelberg (2004)
Burke, R.: Hybrid systems for personalized recommendations. In: Mobasher, B., Anand, S.S. (eds.) ITWP 2003. LNCS (LNAI), vol. 3169, pp. 133–152. Springer, Heidelberg (2005)
Burke, R.: Hybrid web recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Aguzzoli, S., Avesani, P., Massa, P.: Collaborative case-based recommender systems. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, p. 460. Springer, Heidelberg (2002)
Nguyen, A., Denos, N., Berrut, C.: Improving New User Recommendations with Rule-based Induction on Cold User Data. In: ACM conference on Recommender systems, pp. 121–128 (2007)
Ishikawa, M., Geczy, P., Izumi, N., Morita, T., Yamaguchi, T.: Information Diffusion Approach to Cold-Start Problem. In: 2007 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops (wi-iatw), pp. 129–132 (2007)
Rubens, N., Sugiyama, M.: Influence-based collaborative active learning. In: ACM Conference On Recommender Systems, pp. 145–148 (2007)
Burke, R.: Integrating Knowledge-Based and Collaborative-Filtering Recommender Systems. In: AAAI Workshop on AI in Electronic Commerce, pp. 69–72. AAAI, Menlo Park (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Godse, M., Sonar, R., Jadhav, A. (2009). A Hybrid Approach for Knowledge-Based Product Recommendation. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds) Information Systems, Technology and Management. ICISTM 2009. Communications in Computer and Information Science, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00405-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-642-00405-6_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00404-9
Online ISBN: 978-3-642-00405-6
eBook Packages: Computer ScienceComputer Science (R0)