Time-aware location sequence recommendation for cold-start mobile users

Abstract

In this paper, we study the problem of recommending time-sensitive location sequence for mobile users using their check-in data on location-based social networks. Most of the existing studies on Point of Interest (POI) recommendation and prediction fail to address the following two key challenges: (1) how to handle the scenario where the user-location matrix is very sparse (i.e., each user has a very limited number of check-ins, or to say, cold-start users), and (2) how to recommend an optimal time-sensitive visit sequence where each venue matches a time slot specified by users, based on their check-in histories. Motivated by the two challenges above, we propose a predictive framework that enables time-sensitive location sequence recommendation leveraging both the users' semantic and spatial similarities, especially for cold-start users. Our novel framework consists of three modules: semantic similarity modeling, spatial similarity modeling, and on-line sequence recommendation. In semantic modeling, we calculate users' similarity scores by comparing users' temporal hierarchical semantic trees. In spatial modeling, we use Gaussian Mixture Model (GMM) to compute users' similarity scores with respect to their geographical movement paterns. Aferwards, we combine the check-in data of the target user with those of her top-k most similar users in terms of both semantic and spatial similarities to train a personalized Hidden Markov Model (HMM) to predict the most probable venue category for each specified time slot. At last, we recommend location sequence based on the predicted venue category sequence for the target user using geographical mapping.