Season-aware Attraction Recommendation With Dual-trust Enhancement

Tourism industry has witnessed an astonishing growth in recent years.Both tourism operators and individual tourists have benefited from the boom.However,it also poses unprecedented challenges.Attraction recommendation plays an essential role in tourism,such as relieving information overload for tourists and increasing 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,tourism is a seasonal phenomenon and we capture tourists' seasonal topic preferences by analyzing their travel histories along two dimensions:time and attraction.Seasonal topic preferences indicate the tourist preferences for topics of attractions in different seasons.Secondly,because both familiarity-based trust and similarity-based trust play essential role in travel decision making process,we develop a dual-trust relationship(DTR)model based on these two types of 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.Finally,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.