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

• This paper integrates techniques of natural language processing into a case retrieval agent.
• The use of semantic and syntactic information defines the meanings more accurately.
• Integrating semantic-based retrieval agent into the CBR system improves performance at initial state.
• The proposed CBR system with collaborative filtering constantly improves recommendation quality.
• The proposed CBR model outperforms the compared systems in the case study of an online bookstore.

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.