Authors:
Daizong Ding, Mi Zhang, Xudong Pan, Min Yang, Xiangnan He
Publication:
This paper is included in IEEE International Conference on Data Mining, 2020
Abstract:
Location recommendation becomes increasingly important in the mobile era. Particularly, how to exploit personalized geographical preferences determines the quality of recommended results. A number of efforts have been made on this task, however, there exists a common limitation called the outof-town recommending problem, i.e., those far places can hardly be recommended. In this paper, we first reveal why modeling the geographical patterns is difficult with the help of the extreme value theory. We find that out-of-town distances are heavy-tailed variables with few observations and extreme values, making it difficult to use common distributions to describe them. To address this issue, we propose a new function called volcano function to model out-of-town distances and personalize it for different users. Empirical results show that we can learn effective patterns from limited observations. Finally we extend the volcano function to a ranking-based collaborative filtering framework, naming it as volcano network (VolNet). Experimental results show the superior performance of VolNet, especially the recall is improved from 0.2 to 0.35 in recommending remote venues compared with the stateof-the-art method GeoMF++.
Modeling Personalized Out-of-Town Distances in Location Recommendation.pdf