Novelty Learning via Collaborative Proximity Filtering
This addresses the issue of user churn in domains like media consumption by adapting to unobservable preference changes, though it appears incremental as it builds on existing recommender system concepts with specific innovations.
The paper tackles the problem of spontaneous changes in user preferences in recommender systems, which are not directly observable and can lead to user churn, by developing a model that learns and tracks latent user tastes to provide tailored novelty, resulting in a framework that adaptively offers variety based on individualized policies.
The vast majority of recommender systems model preferences as static or slowly changing due to observable user experience. However, spontaneous changes in user preferences are ubiquitous in many domains like media consumption and key factors that drive changes in preferences are not directly observable. These latent sources of preference change pose new challenges. When systems do not track and adapt to users' tastes, users lose confidence and trust, increasing the risk of user churn. We meet these challenges by developing a model of novelty preferences that learns and tracks latent user tastes. We combine three innovations: a new measure of item similarity based on patterns of consumption co-occurrence; model for {\em spontaneous} changes in preferences; and a learning agent that tracks each user's dynamic preferences and learns individualized policies for variety. The resulting framework adaptively provides users with novelty tailored to their preferences for change per se.