7 papers
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…
Online Data Selection for Instruction Tuning via Gaussian Processes
Jun Wang, Quoc Phong Nguyen, Julien Monteil +1
With Large Language Model (LLM) pre-training and fine-tuning shifting its focus from data volume to data quality, quality data selection has emerged as a critical research topic. E…
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…
On the Mechanisms of Collaborative Learning in VAE Recommenders
Tung-Long Vuong, Julien Monteil, Hien Dang +3
Variational Autoencoders (VAEs) are a powerful alternative to matrix factorization for recommendation. A common technique in VAE-based collaborative filtering (CF) consists in appl…
Learning Visual Hierarchies in Hyperbolic Space for Image Retrieval
Ziwei Wang, Sameera Ramasinghe, Chenchen Xu +3
Structuring latent representations in a hierarchical manner enables models to learn patterns at multiple levels of abstraction. However, most prevalent image understanding models f…
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…