Boosting item findability: Bridging the semantic gap between search phrases and item descriptions

Hung V. Nguyen, Hasan Davulcu, V. Ramachandran

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Most search engines do their text query and retrieval using keywords. However, vendors cannot anticipate all possible ways in which shoppers search for their products. In fact, many times, there may be no direct keyword match between a search phrase and descriptions of products that are perfect hits for the search. A highly automated solution to the problem of bridging the semantic gap between product descriptions and search phrases used by Web shoppers is developed. By using scalable information extraction techniques from Web sources and a frequent itemset mining algorithm, our system can learn how meanings can be ascribed to popular search phrases with dynamic connotations. By annotating the product databases based on the meanings of search phrases mined by our system, catalog owners can boost the findability of their products.

Original languageEnglish (US)
Pages (from-to)1-20
Number of pages20
JournalInternational Journal of Intelligent Information Technologies
Volume2
Issue number3
DOIs
StatePublished - Jan 1 2006

Keywords

  • Frequent itemset mining
  • Information extraction
  • Labeling
  • Phrase mining
  • Web mining

ASJC Scopus subject areas

  • Information Systems
  • Decision Sciences (miscellaneous)

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