Abstract
Content-based recommender systems (CBRS) and collaborative filtering are the type of recommender systems most spread in the e-commerce arena. A CBRS works with two sets of information: (i) a set of features that describe the items to be recommended and (ii) a user’s profile built from past choices that the user made over a subset of items. Based on these sets and on weighting items features the CBRS is able to recommend those items that better fits the user profile. Commonly, a CBRS deals with simple item features such as key words extracted from the item description applying a simple feature weighting model, based on the TF-IDF. However, this method does not obtain good results when features are assessed in multiple values and or domains. In this contribution we propose a higher level feature weighting method based on entropy and coefficients of correlation and contingency in order to improve the content-based filtering in settings with multi-valued features.
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
Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans. on Knowledge and Data Engineering 17(6), 734–749 (2005)
Aizawa, A.: An information-theoretic perspective of TF-IDF measures. Information Processing and Managemente 39, 45–65 (2003)
Bishop, Y.M.M., Fienberg, S.E., Holland, P.W.: Discrete Multivariate Analysis: Theory and Practice. The MIT Press, England (1995)
Bogers, T., Bosch, A.: Comparing an evaluating information retrieval algorithms for news recommendation. In: Proc. of the 2007 ACM Conference on Recommender Systems, Minneapolis, USA, pp. 141–144 (2007)
Chung Wu, H., Pong Luk, R.W.: Interpreting tf-idf term weights as making relevance decisions. ACM Trans. on Information Systems 26(3), Article No. 13, 1–37 (2008)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)
Fang, H., Tao, T., Zhai, C.: A formal study of information retrieval heuristics. In: Proc. of the 27th annual int. ACM SIGIR conf. on Research and depvelopment in information retrieval, pp. 49–56 (2004)
Hayes, C., Massa, P., Avesani, P., Cunningham, P.: An On-line Evaluation Framework for Recommender Systems. Technical Report TCD-CS-2002-19, Department of Computer Science, Trinity College Dublin (2002)
Hong, T.P., Chen, J.B.: Finding relevant attributes and membership functions. Fuzzy Sets and Systems 103, 389–404 (1999)
John, G.H., Kohavi, R., Pfleger, K.: Irrelevant features and the subset selection problem. In: Machine Learning: Proc. of the 11th int. conf., pp. 121–129. Morgan Kaufmann Publishers, San Francisco (1994)
Martínez, L., Pérez, L.G., Barranco, M.J.: A Multi-granular Linguistic Content-Based Recommendation Model. International Journal of Intelligent Systems 22(5), 419–434 (2007)
Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proc. of the 15th ACM conf. on Digital libraries, Texas, USA, pp. 195–204 (2000)
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)
Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)
Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Feature-weighted user model form recommender systems. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 97–106. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barranco, M.J., Martínez, L. (2010). A Method for Weighting Multi-valued Features in Content-Based Filtering. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-13033-5_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13032-8
Online ISBN: 978-3-642-13033-5
eBook Packages: Computer ScienceComputer Science (R0)