The general aim of the recommender system is to provide personalized suggestions to
users, which is opposed to suggesting popular items. However, the normal training …

In graph classification, attention-and pooling-based graph neural networks (GNNs) prevail to
extract the critical features from the input graph and support the prediction. They mostly …

We tackle the task of video moment retrieval (VMR), which aims to localize a specific
moment in a video according to a textual query. Existing methods primarily model the …

Recommender systems usually amplify the biases in the data. The model learned from
historical interactions with imbalanced item distribution will amplify the imbalance by over …

Learning user representations based on historical behaviors lies at the core of modern
recommender systems. Recent advances in sequential recommenders have convincingly …

Modern recommender systems learn user representations from historical interactions, which
suffer from the problem of user feature shifts, such as an income increase. Historical …

In machine learning, exploring data correlations to predict outcomes is a fundamental task.
Recognizing causal relationships embedded within data is pivotal for a comprehensive …

The training and inference of Graph Neural Networks (GNNs) are costly when scaling up to
large-scale graphs. Graph Lottery Ticket (GLT) has presented the first attempt to accelerate …

Recommender systems usually face the issue of filter bubbles: over-recommending
homogeneous items based on user features and historical interactions. Filter bubbles will …

Composing text and image for image retrieval (CTI-IR) is a new yet challenging task, for
which the input query is not the conventional image or text but a composition, ie, a reference …