Traditional recommender systems typically use user-item rating histories as their main data
source. However, deep generative models now have the capability to model and sample …

Generative models such as Generative Adversarial Networks (GANs) and Variational Auto-
Encoders (VAEs) are widely utilized to model the generative process of user interactions …

Existing research on fairness-aware recommendation has mainly focused on the
quantification of fairness and the development of fair recommendation models, neither of …

This paper explores the biases inherent in ChatGPT-based recommender systems, focusing
on provider fairness (item-side fairness). Through extensive experiments and over a …

Reinforcement Learning (RL)-based recommender systems (RSs) have garnered
considerable attention due to their ability to learn optimal recommendation policies and …

Feed recommendation systems, which recommend a sequence of items for users to browse
and interact with, have gained significant popularity in practical applications. In feed …

As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects
user experience and satisfaction by rearranging the input ranking lists, and thereby plays a …

Personalized recommender systems fulfill the daily demands of customers and boost online
businesses. The goal is to learn a policy that can generate a list of items that matches the …

Recent research has employed reinforcement learning (RL) algorithms to optimize long-term
user engagement in recommender systems, thereby avoiding common pitfalls such as user …

This paper presents LightLM, a lightweight Transformer-based language model for
generative recommendation. While Transformer-based generative modeling has gained …