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Implementation of a Chatbot Application for Restaurant recommendation using Statistical Word Comparison Method  

Min, Dong-Hee (Department of Information and Communication Engineering, Sangji University)
Lee, Woo-Beom (Department of Information and Communication Engineering, Sangji University)
Publication Information
Journal of the Institute of Convergence Signal Processing / v.20, no.1, 2019 , pp. 31-36 More about this Journal
Abstract
A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.
Keywords
Chatbot; Restaurant Recommendation; Statistical Word Comparison; Accessibility measurement;
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