Affiliations: [a] Department of Management Science and Engineering, School of Economics and Management, Southeast University, Jiangsu, Nanjing, China
| [b] Department of Information Management, School of Economics and Management, Nanjing University of Information Science & Technology, Jiangsu, Nanjing, China
| [c] Department of Tourism, School of Humanities, Southeast University, Jiangsu, Nanjing, China
Correspondence:
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Corresponding author. Chao Huang, Department of Management Science and Engineering, School of Economics and Management, Southeast University, Jiangsu, Nanjing 210096, China. Tel.: +86 138 1406 9012; E-mail: [email protected].
Abstract: Attraction recommendation plays an essential role in tourism. For example, it can relieve information overload for tourists and increase sales for tourism operators. When making travel decisions, tourists depend heavily on the personal preferences and suggestions from people they trust. However, most existing attraction recommendation methods focus on the tourist preferences for topics of attractions, yet overlook the seasonality in topic preferences. Additionally, extant studies are generally based on a single type of trust, which may represent trust relations inaccurately. In order to overcome these issues, we propose a novel season-aware attraction recommendation method based on the seasonal topic preferences and dual-trust relations. Firstly, we capture tourists’ seasonal topic preferences by analyzing their travel histories along two dimensions: time and attraction. Secondly, we develop a dual-trust relationship (DTR) model based on familiarity-based trust and similarity-based trust, in contrast to existing studies that purely focus on a single type of trust. Thirdly, we propose a novel season-aware attraction recommendation method named SAR-DTR. In a specific season, it predicts ratings based on both topic preferences in the given season and suggestions from tourists they trust. To demonstrate the superiority of the proposed method to other approaches, an empirical study with real-world data was conducted. The experimental results regarding both prediction and recommendation performance are reported.
