Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …

Representation learning on user-item graph for recommendation has evolved from using
single ID or interaction history to exploiting higher-order neighbors. This leads to the …

The learn-to-compare paradigm of contrastive representation learning (CRL), which
compares positive samples with negative ones for representation learning, has achieved …

Graph neural networks (GNNs) have recently emerged as state-of-the-art collaborative
filtering (CF) solution. A fundamental challenge of CF is to distill negative signals from the …

Federated learning (FL) is quickly becoming the de facto standard for the distributed training
of deep recommendation models, using on-device user data and reducing server costs. In a …

This paper provides a unified account of two schools of thinking in information retrieval
modelling: the generative retrieval focusing on predicting relevant documents given a query …

This paper studies the multi-modal recommendation problem, where the item multi-modality
information (eg, images and textual descriptions) is exploited to improve the …

Graph representation learning has been extensively studied in recent years, in which
sampling is a critical point. Prior arts usually focus on sampling positive node pairs, while the …

Properly handling missing data is a fundamental challenge in recommendation. Most
present works perform negative sampling from unobserved data to supply the training of …

Recommending products to users means estimating their preferences for certain items over
others. This can be cast either as a problem of estimating the rating that each user will give …