Exploiting Ranking Consistency Principle in Representation Learning for Location Promotion

Location-based services, which use information of people’s geographical position as service context, are becoming part of our daily life. Given the large volume of heterogeneous data generated by location-based services, one important problem is to estimate the visiting probability of users who haven’t visited a target Point of Interest (POI) yet, and return the target user list based on their visiting probabilities. This problem is called the location promotion problem. The location promotion problem has not been well studied due to the following difficulties: (1) the cold start POI problem: a target POI for promotion can be a new POI with no check-in records; and (2) heterogeneous information integration. Existing methods mainly focus on developing a general mobility model for all users’ check-ins, but ignore the ranking utility from the perspective of POIs and the interaction between geographical and preference influence of POIs.

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