This paper is an early-stage work on recommending the durations of stay at a destination. It was published in the 2019 ACM RecSys Late-breaking Results track.
Recommender systems could benefit from not only recommending the most fitting items but also in what quantity the user should consume them. In this paper, we tackle the problem of recommending the personalized duration of stay at a destination. We present a data-driven solution to this problem based on mining trips from location-based social networks. To determine the recommended duration of stay at a destination, we use a statistical approach based on how long travelers typically stay in different cities and how much time the current user generally spends visiting cities. The method can serve as an extension of personalized travel planning systems by not just recommending which city one should travel to but also how much time to spend there.
Linus W. Dietz and Wolfgang Wörndl. “How Long to Stay Where? On the Amount of Item Consumption in Travel Recommendation.” In: ACM RecSys 2019 Late-breaking Results. Sept. 2019, pp. 31–35