Composite match autocompletion (COMMA): A semantic result-oriented autocompletion technique for e-marketplaces

Abstract

Autocompletion systems support users in the formulation of queries in different situations, 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 supports 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 it is not associated to a sensible increase of computational costs.

Session-based Suggestion of Topics for Geographic Exploratory Search

“…Working at the conceptual level, our work is also complementary to the research on exploration vs. exploitation in information retrieval, which provides models to recognize the type of search that the user is performing, and/or to adapt the search results accordingly; e.g., [34,5,31].…”

Concept-aware geographic information retrieval

“…Finally, recent search auto-completion models, such as COMMA [21], support the search of items in catalogs by indexing information items and by applying string-matching algorithms for item selection. Our work di ers in two main aspects: rstly, we rely on item classi cation in ontology concepts to reduce the amount of pre-processing work to be done by the system.…”