Integrating a semantic-based retrieval agent into case-based reasoning systems: A case study of an online bookstore

Jia Wei Chang, Ming Che Lee, Tzone I. Wang

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Natural language search engines should be developed to provide a friendly environment for business-to-consumer e-commerce that reduce the fatigue customers experience and help them decide what to buy. To support product information retrieval and reuse, this paper presents a novel framework for a case-based reasoning system that includes a collaborative filtering mechanism and a semantic-based case retrieval agent. Furthermore, the case retrieval agent integrates short-text semantic similarity (STSS) and recognizing textual entailment (RTE). The proposed approach was evaluated using competitive methods in the performance of STSS and RTE, and according to the results, the proposed approach outperforms most previously described approaches. Finally, the effectiveness of the proposed approach was investigated using a case study of an online bookstore, and according to the results of case study, the proposed approach outperforms a compared system using string similarity and an existing e-commerce system, Amazon.

Original languageEnglish
Pages (from-to)29-42
Number of pages14
JournalComputers in Industry
Volume78
DOIs
Publication statusPublished - 2016 May 1

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering

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