Natural Language Assistant: A Dialog System for Online Product Recommendation

Joyce Chai, Veronika Horvath, Nicolas Nicolov, Margo Stys, Nanda Kambhatla, Wlodek Zadrozny, Prem Melville

Abstract


With the emergence of electronic-commerce systems, successful information access on electroniccommerce web sites becomes essential. Menu-driven navigation and keyword search currently provided by most commercial sites have considerable limitations because they tend to overwhelm and frustrate users with lengthy, rigid, and ineffective interactions. To provide an efficient solution for information access, we have built the NATURAL language ASSISTANT (NLA), a web-based natural language dialog system to help users find relevant products on electronic-commerce sites. The system brings together technologies in natural language processing and human-computer interaction to create a faster and more intuitive way of interacting with web sites. By combining statistical parsing techniques with traditional AI rule-based technology, we have created a dialog system that accommodates both customer needs and business requirements. The system is currently embedded in an application for recommending laptops and was deployed as a pilot on IBM's web site.

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DOI: http://dx.doi.org/10.1609/aimag.v23i2.1641

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