Recommender systems have been widely applied to several domains and applications to
assist decision making by recommending items tailored to user preferences. One of the …

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 …

As a highly data-driven application, recommender systems could be affected by data bias,
resulting in unfair results for different data groups, which could be a reason that affects the …

Recently, recommender systems are widely used on various platforms in real world to
provide personalized recommendations. However, sparsity is a tough problem in a …

Recommender systems deployed in real-world applications can have inherent exposure
bias, which leads to the biased logged data plaguing the researchers. A fundamental way to …

Recent advancements in foundation models such as large language models (LLM) have
propelled them to the forefront of recommender systems (RS). Moreover, fairness in RS is …

The issue of fairness in recommendation is becoming increasingly essential as
Recommender Systems (RS) touch and influence more and more people in their daily lives …

Multi-objective optimization (MOO) and multi-task learning (MTL) have gained much
popularity with prevalent use cases such as production model development of regression …

Deep learning models have shown their vulnerability when dealing with adversarial attacks.
Existing attacks almost perform on low-level instances, such as pixels and super-pixels, and …

Recommender Systems have been playing essential roles in e-commerce portals. Existing
recommendation algorithms usually learn the ranking scores of items by optimizing a single …