Authors:
Hasan Davulcu
;
Hung V. Nguyen
and
Viswanathan Ramachandran
Affiliation:
Arizona State University Tempe, United States
Keyword(s):
E-commerce, Data Mining, Frequent Itemsets, Web Data, Information Retrieval, Information Extraction, Relevance Feedback.
Related
Ontology
Subjects/Areas/Topics:
B2B, B2C and C2C
;
B2C/B2B Considerations
;
Business and Social Applications
;
Case Studies
;
Communication and Software Technologies and Architectures
;
e-Business
;
Enterprise Information Systems
;
e-Procurement and Web-Based Supply Chain Management
;
Health Engineering and Technology Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Simulation and Modeling
;
Simulation Tools and Platforms
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Web Information Systems and Technologies
Abstract:
Most search engines do their text query and retrieval based on keyword phrases. However, publishers cannot anticipate all possible ways in which users search for the items in their documents. In fact, many times, there may be no direct keyword match between a search phrase and descriptions of items that are perfect “hits” for the search. We present a highly automated solution to the problem of bridging the semantic gap between item information and search phrases. Our system can learn rule-based definitions that can be ascribed to search phrases with dynamic connotations by extracting structured item information from product catalogs and by utilizing a frequent itemset mining algorithm. We present experimental results for a realistic e-commerce domain. Also, we compare our rule-mining approach to vector-based relevance feedback retrieval techniques and show that our system yields definitions that are easier to validate and perform better.