Dynamic Local Models for Online Recommendation

With the explosion of the volume of user-generated data, designing online recommender systems that learn from data streams has become essential. These systems rely on incremental learning that continuously update models as new observations arrive and they should be able to adapt to drifts in real-time. User preferences evolve over time and tracking their evolution is not an easy task. In addition to the low number of observations available per user, the preferences change at different moments and in different ways for each individual. In this paper, we propose a novel approach based on local models to address this problem. Local models are known for their ability to capture diverse preferences among user subsets. Our approach automatically detects the drift of preferences that leads a user to adopt a behavior closer to the users of another subset, and adjusts the models accordingly. Our experiments on real world datasets show promising results and prove the effectiveness of using local models to adapt to changes in user preferences.