Recommendation models that utilize unique identities (IDs for short) to represent distinct
users and items have been state-of-the-art (SOTA) and dominated the recommender …

To alleviate the problem of information explosion, recommender systems are widely
deployed to provide personalized information filtering services. Usually, embedding tables …

Human motion prediction is a classical problem in computer vision and computer graphics,
which has a wide range of practical applications. Previous effects achieve great empirical …

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …

Due to underlying privacy-sensitive information in user-item interaction data, the risk of
privacy leakage exists in the centralized-training recommender system (RecSys). To this …

Multi-task learning (MTL) has gained significant popularity in recommender systems as it
enables the simultaneous optimization of multiple objectives. A key challenge in MTL is …

Recommendation has become a prominent area of research in the field of Information
Retrieval (IR). Evaluation is also a traditional research topic in this community. Motivated by …

Two-tower models are a prevalent matching framework for recommendation, which have
been widely deployed in industrial applications. The success of two-tower matching …

Reproducibility of recommender systems research has come under scrutiny during recent
years. Along with works focusing on repeating experiments with certain algorithms, the …

Click Through Rate (CTR) prediction plays an essential role in recommender systems and
online advertising. It is crucial to effectively model feature interactions to improve the …