Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates

TL;DR

This paper introduces a novel hybrid recommendation algorithm that effectively balances the recommendation quality for warm items with the promotion of cold items, addressing a key challenge in cold start scenarios.

Contribution

The paper presents a new method using tunable stochastic gates to harmonize the conflicting goals of accuracy for warm items and promotion of cold items in hybrid recommenders.

Findings

Effective promotion of cold items demonstrated across multiple domains

Trade-off analysis between cold item promotion and warm item accuracy

Algorithm outperforms existing methods in cold start scenarios

Abstract

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.