Reinforcement learning serves as a potent tool for modeling dynamic user interests within
recommender systems, garnering increasing research attention of late. However, a …

Planning for both immediate and long-term benefits becomes increasingly important in
recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning …

While personalization increases the utility of recommender systems, it also brings the issue
of filter bubbles. eg, if the system keeps exposing and recommending the items that the user …

Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging
historical user-item interactions to provide personalized suggestions. However, CF-based …

Extracting users' interests from their behavior, particularly their 1-hop neighbors, has been
shown to enhance Click-Through Rate (CTR) prediction performance. However, online …

Reinforcement Learning (RL)-Based Recommender Systems (RSs) have gained rising
attention for their potential to enhance long-term user engagement. However, research in …

In recommendation systems (RS), user behavior data is observational rather than
experimental, resulting in widespread bias in the data. Consequently, tackling bias has …

Implicit feedback (eg, user clicks) is widely used in building recommender systems (RS).
However, the inherent notorious exposure bias significantly affects recommendation …

With the capacity to capture high-order collaborative signals, Graph Neural Networks
(GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their …

The contextual bandit has been identified as a powerful framework to formulate the
recommendation process as a sequential decision-making process, where each item is …