DTCRSKG: A Deep Travel Conversational Recommender System Incorporating Knowledge Graph

Hui Fang, Chongcheng Chen, Yunfei Long, Ge Xu and Yongqiang Xiao
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Hui Fang: Key Laboratory of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou 350108, China
Chongcheng Chen: Key Laboratory of Spatial Data Mining and Information Sharing of MOE, Fuzhou University, Fuzhou 350108, China
Yunfei Long: School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK
Ge Xu: Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
Yongqiang Xiao: Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China

Mathematics, 2022, vol. 10, issue 9, 1-16

Abstract: In the era of information explosion, it is difficult for people to obtain their desired information effectively. In tourism, a travel recommender system based on big travel data has been developing rapidly over the last decade. However, most work focuses on click logs, visit history, or ratings, and dynamic prediction is absent. As a result, there are significant gaps in both dataset and recommender models. To address these gaps, in the first step of this study, we constructed two human-annotated datasets for the travel conversational recommender system. We provided two linked data sets, namely, interaction sequence and dialogue data sets. The usage of the former data set was done to fully explore the static preference characteristics of users based on it, while the latter identified the dynamics changes in user preference from it. Then, we proposed and evaluated BERT-based baseline models for the travel conversational recommender system and compared them with several representative non-conversational and conversational recommender system models. Extensive experiments demonstrated the effectiveness and robustness of our approach regarding conversational recommendation tasks. Our work can extend the scope of the travel conversational recommender system and our annotated data can also facilitate related research.

Keywords: tourism domain; conversational recommender; knowledge graph; BERT-based (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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