4 papers · 1 filter
BACH: A Bayesian Admixture of Contrastive Heads for Multi-Interest Two-Tower Retrieval
Quoc Phong Nguyen, Paul Albert, Long Vuong +2
Two-tower retrievers compress each user into a single embedding, limiting their ability to serve diverse interests. Multi-interest models give each user several heads scored by a m…
FOSTER: First-order Dataset Distillation for Text-based Sequential Recommendation
Hung Vinh Tran, Tong Chen, Xinyi Gao +3
Text-based sequential recommender systems, while greatly improving recommendation accuracy by incorporating item contexts, are undeniably more expensive to train. By condensing a l…
Geometric Collaborative Filtering with Convergence
Hisham Husain, Julien Monteil
Latent variable collaborative filtering methods have been a standard approach to modelling user-click interactions due to their simplicity and effectiveness. However, there is limi…
MARec: Metadata Alignment for cold-start Recommendation
Julien Monteil, Volodymyr Vaskovych, Wentao Lu +2
For many recommender systems, the primary data source is a historical record of user clicks. The associated click matrix is often very sparse, as the number of users x products can…