8 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…
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…
Personalised Outfit Recommendation via History-aware Transformers
Myong Chol Jung, Julien Monteil, Philip Schulz +1
We present the history-aware transformer (HAT), a transformer-based model that uses shoppers' purchase history to personalise outfit predictions. The aim of this work is to recomme…
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…