A Multistage Ranking Strategy for Personalized Hotel Recommendation with Human Mobility Data

To increase user satisfaction and own income, more and more hotel booking sites begin to pay attention to personalized recommendation. However, almost all user preference information only comes from the user actions in the hotel reservation scenario. Obviously, this approach has its limitations in particular in situation of user cold start, i.e., when only little information is available about an individual user. In this paper, we focus on the hotel recommendation in mobile map applications, which has abundant human mobility data to provide extra personalized information for hotel search ranking. For this purpose, we propose a personalized multistage pairwise learning-to-ranking model, which can capture more personalized information by utilizing full scenarios hotel click data of users in map applications. At the same time, the multistage model can effectively solve the problem of cold start. Both offline and online evaluation results show that the proposed model significantly outperforms multiple strong baseline methods.