Abstract
There are too many products in an on-line shopping website. We need to help buyers to find products they want in an efficient way. A keyword-based IR system seems suitable for searching products. Unfortunately, we observe from real world query logs and find that queries for product search are usually very short. What is worse, a document described a product may have lots of words of related products. It is hard for an IR system to distinguish representative terms from other noisy terms. Hence, we propose a supervised learning method to realize semantic types of each term in product document titles. Then we modify Language Model to improve the relevance of search results. Our methods have significant improvement in search result precision in real world document collection and query collections.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
LIBSVM: a library for support vector machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm
Gil, A., Garcíai, F.: E-Commerce Recommenders: Powerful Tools for E-business. Crossroads 10(2), 6–6 (2003)
Jammalamadaka, R.C., Chittar, N., Ghatare, S.: Mining Product Intention Rules from Transaction Logs of an Ecommerce Portal. In: Proceedings of the 2009 International Database Engineering and Applications Symposium, pp. 311–314. ACM, New York (2009)
Kim, Y.S., Yum, B.J., Song, J., Kim, S.M.: Development of a recommender system based on navigational and behavioral patterns of customers in e-commerce sites. Expert Syst. Appl. 28(2), 381–393 (2005)
Lin, R., Kraus, S., Tew, J.: Attaining Fast and Successful Searches in E-commerce Environments. In: Sebastiani, F. (ed.) ECIR 2003. LNCS, vol. 2633, pp. 120–134. Springer, Heidelberg (2003)
Lin, R., Kraus, S., Tew, J.: OSGS - A Personalized Online Store for E-commerce Environments. Inf. Retr. 7(3-4), 369–394 (2004)
Parikh, N., Sundaresan, N.: Inferring Semantic Query Relations from Collective User Behavior. In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, pp. 349–358. ACM, New York (2008)
Parikh, N., Sundaresan, N.: Scalable and Near Real-Time Burst Detection from eCommerce Queries. In: Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 972–980. ACM, New York (2008)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-Commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158–167. ACM, New York (2000)
Wang, H.F., Wu, C.T.: A strategy-oriented operation module for recommender systems in E-commerce. In: Proceedings of the 9th WSEAS International Conference on Applied Informatics and Communications, pp. 78–83. WSEAS Stevens Point, Wisconsin (2009)
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
Chen, CW., Cheng, PJ. (2010). Title-Based Product Search – Exemplified in a Chinese E-commerce Portal. In: Cheng, PJ., Kan, MY., Lam, W., Nakov, P. (eds) Information Retrieval Technology. AIRS 2010. Lecture Notes in Computer Science, vol 6458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17187-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-17187-1_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17186-4
Online ISBN: 978-3-642-17187-1
eBook Packages: Computer ScienceComputer Science (R0)