As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual
improvement among tasks considering their shared knowledge. It is an important topic in …

Multi-task learning (MTL) models have demonstrated impressive results in computer vision,
natural language processing, and recommender systems. Even though many approaches …

Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously.
A recent development known as task arithmetic has revealed that several models, each fine …

Causal inference has recently garnered significant interest among recommender system
(RS) researchers due to its ability to dissect cause-and-effect relationships and its broad …

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 …

Modern recommender systems often deal with a variety of user interactions, eg, click,
forward, purchase, etc., which requires the underlying recommender engines to fully …

Most existing methods mainly input images into a CNN backbone to obtain image features.
However, compared with convolutional features, the recently emerging transformer features …

Multi-scenario and multi-task recommendation can use various feedback behaviors of users
in different scenarios to learn users' preferences and then make recommendations, which …

Multimodal learning methods with targeted unimodal learning objectives have exhibited
their superior efficacy in alleviating the imbalanced multimodal learning problem. However …