Novelty Learning via Collaborative Proximity Filtering

TL;DR

This paper introduces a novel recommender system model that learns and adapts to spontaneous, latent changes in user preferences, enhancing personalization by tracking dynamic tastes and providing tailored novelty.

Contribution

It presents a new similarity measure, models spontaneous preference changes, and develops an adaptive learning agent for personalized novelty recommendations.

Findings

Effective tracking of latent preference changes.

Improved user engagement through tailored novelty.

Adaptive policies enhance recommendation relevance.

Abstract

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.