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

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 …

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

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

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

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 rapid advancement of large language models (LLMs), the diversity of multi-LLM
tasks and the variability in their pricing structures have become increasingly important, as …

In recent years, interactive recommender systems (IRSs) have attracted extensive interest.
Existing IRSs are typically implemented with offline reinforcement learning (RL). They are …

Abstract Session-Based Recommendations (SBR) have become a crucial branch in the
recommendation field. Usually, most previous SBR methods only recommend items existing …