Next POI Recommender System: Multi-view Representation Learning for Outstanding Performance in Various Context

Abstract

Location-based Social Networks (LBSNs) are software service that enable a user to find knowledge and to socialize with other users by offering other user's contents (e.g. reviews, photos, etc.) to a user. This LBSNs have many sub-fields, but Point-of-Interest (POI) recommendation is the most important. Because it is related to the growth of Small and Medium Enterprise (SME) by increasing visitation rate. Generally, it should be possible to respond to various contexts of users in POI recommendation. These contexts are very various and complex, but we define mainly three contexts based on user behavior in local domain. However, each context is defined by different user behavior, so each model and performance are different on various evaluation criteria. In other words, no model is outstanding in all contexts. Therefore, this paper introduces how to define each context, how to make POI embedding for recommendation in empirical multi-view representation learning technique, and how to make optimized POI embedding which is outstanding performance in all contexts of POI recommendation, for various downstream tasks.