Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates
This addresses the cold start problem for recommender systems by enabling controlled integration of new items, though it is incremental in improving hybrid methods.
The paper tackles the challenge of balancing performance on warm items with promoting cold items in hybrid recommenders, showing these objectives conflict and proposing a novel algorithm that achieves a tunable trade-off, demonstrated across movies, apps, and articles with empirical analysis.
A major challenge in collaborative filtering methods is how to produce recommendations for cold items (items with no ratings), or integrate cold item into an existing catalog. Over the years, a variety of hybrid recommendation models have been proposed to address this problem by utilizing items' metadata and content along with their ratings or usage patterns. In this work, we wish to revisit the cold start problem in order to draw attention to an overlooked challenge: the ability to integrate and balance between (regular) warm items and completely cold items. In this case, two different challenges arise: (1) preserving high quality performance on warm items, while (2) learning to promote cold items to relevant users. First, we show that these two objectives are in fact conflicting, and the balance between them depends on the business needs and the application at hand. Next, we propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives and enables a harmonized balance between preserving high accuracy for warm items while effectively promoting completely cold items. We demonstrate the effectiveness of the proposed algorithm on movies, apps, and articles recommendations, and provide an empirical analysis of the cold-warm trade-off.