TOWARDS EFFECTIVE MODELING OF SUCCESSIVE PREFERENCES

Abstract

The increased popularity of web-based platforms, has led to an unprecedented surge of users who perform various activities on these platforms, and therefore, the availability of massive user-related datasets which describe their latent preferences and behaviors. For instance, a rising research interest looks at the modeling of successive preferences from users' sequential data streams, to best learn and provide recommendations, such as recommending the next Point-of-Interest (POI) or location to users from their historical successive visit patterns. However, effective modeling of users' successive preferences remains a challenging task due to various hurdles, which include balancing the explore-exploit trade-offs, data sparsity, the highly dynamic behavioral patterns of users, and others. In this thesis, we will look at three main approaches to model successive preferences, with different location recommendation applications.