Autocompletion systems support users in the formulation of queries in different computer systems, from development environments to the web. In this paper we describe Composite Match Autocompletion (COMMA), a lightweight approach to the introduction of semantics in the realization of a semi-structured data autocompletion matching algorithm. The approach is formally described, then it is applied and evaluated with specific reference to the e-commerce context. The semantic extension to the matching algorithm exploits available information about product categories and distinguishing features of products to enhance the elaboration of exploratory queries. COMMA sup- ports a seamless management of both targeted/precise queries and exploratory/vague ones, combining different filtering and scoring techniques. The algorithm is evaluated with respect both to effectiveness and efficiency in a real-world scenario: the achieved improvement is significant and not associated to a sensible increase of computational costs
Palmonari, M., Vizzari, G., Broglia, A., Lamberti, N., Porrini, R. (2012). COMMA: A Result-oriented Composite Autocompletion Method for e-Marketplaces. In Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2012 (pp.545-552). IEEE Computer Society press [10.1109/WI-IAT.2012.217].
COMMA: A Result-oriented Composite Autocompletion Method for e-Marketplaces
PALMONARI, MATTEO LUIGI;VIZZARI, GIUSEPPE;PORRINI, RICCARDO
2012
Abstract
Autocompletion systems support users in the formulation of queries in different computer systems, from development environments to the web. In this paper we describe Composite Match Autocompletion (COMMA), a lightweight approach to the introduction of semantics in the realization of a semi-structured data autocompletion matching algorithm. The approach is formally described, then it is applied and evaluated with specific reference to the e-commerce context. The semantic extension to the matching algorithm exploits available information about product categories and distinguishing features of products to enhance the elaboration of exploratory queries. COMMA sup- ports a seamless management of both targeted/precise queries and exploratory/vague ones, combining different filtering and scoring techniques. The algorithm is evaluated with respect both to effectiveness and efficiency in a real-world scenario: the achieved improvement is significant and not associated to a sensible increase of computational costs
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