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 …

Cold-start problem is still a very challenging problem in recommender systems. Fortunately,
the interactions of the cold-start users in the auxiliary source domain can help cold-start …

Accurate estimation of post-click conversion rate is critical for building recommender
systems, which has long been confronted with sample selection bias and data sparsity …

Multi-behavior recommendation (MBR) aims to jointly consider multiple behaviors to
improve the target behavior's performance. We argue that MBR models should:(1) model the …

Multi-task learning has been widely applied in computational vision, natural language
processing and other fields, which has achieved well performance. In recent years, a lot of …

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

Multi-task Recommender Systems (MTRSs) has become increasingly prevalent in a variety
of real-world applications due to their exceptional training efficiency and recommendation …

Building appropriate scenarios to meet the personalized demands of different user groups is
a common practice. Despite various scenario brings personalized service, it also leads to …

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 …

Multi-scenario learning (MSL) enables a service provider to cater for users' fine-grained
demands by separating services for different user sectors, eg, by user's geographical region …