Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation

Abstract

The cold start problem is a long-standing challenge in recommender systems. That is, how to recommend for new users and new items without any historical interaction record? Recent ML-based approaches have made promising strides versus traditional methods. These ML approaches typically combine both user-item interaction data of existing warm start users and items (as in CF-based methods) with auxiliary information of users and items such as user profiles and item content information (as in content-based methods). However, such approaches face key drawbacks including the error superimposition issue that the auxiliary-to-CF transformation error increases the final recommendation error; the ineffective learning issue that long distance from transformation functions to model output layer leads to ineffective model learning; and the unified transformation issue that applying the same transformation function for different users and items results in poor transformation. Hence, this paper proposes a novel model designed to overcome these drawbacks while delivering strong cold start performance. Three unique features are: (i) a combined separate-training and joint-training framework to overcome the error superimposition issue and improve model quality; (ii) a Randomized Training mechanism to promote the effectiveness of model learning; and (iii) a Mixture-of-Experts Transformation mechanism to provide 'personalized' transformation functions. Extensive experiments on three datasets show the effectiveness of the proposed model over state-of-the-art alternatives.