Personalized Tour Recommendation via Analyzing User Tastes for Travel Distance, Diversity and Popularity

The goal of a tour recommendation is to recommend the best destinations according to the preferences of each tourist. The task of tour recommendation is challenging in that it not only has to consider the ratings, as do existing traditional recommendation problems, but it must also consider the personalization of the unique characteristics, such as diversity, travel distance, and popularity of the travel destination, which previous studies have failed to take into account. In this paper, we propose, for the first time, aspect personalization: we find out how important each user considers the diversity, distance and popularity of a travel destination when choosing where to visit. Then, we provide recommendations on tourist attractions by combining the personalized score for each factor and the predicted score. For the evaluation, we gathered user ratings and metadata of POIs from TripAdvisor and Naver. Experimental results showed that the proposed method had an 82%, 24% and 20% improvement in precision and a 129%, 35% and 22% improvement in recall in terms of top-1, top-2 and top-3 recommendations