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 …

Sequential recommendation aims to recommend the next item that matches a user'sinterest,
based on the sequence of items he/she interacted with before. Scrutinizingprevious studies …

Leveraging Large Language Models as Recommenders (LLMRec) has gained significant
attention and introduced fresh perspectives in user preference modeling. Existing LLMRec …

Sequential recommendation (SRS) has become the technical foundation in many
applications recently, which aims to recommend the next item based on the user's historical …

Recommender systems play a crucial role in tackling the challenge of information overload
by delivering personalized recommendations based on individual user preferences. Deep …

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the
noise issues from data cleaning perspective such as data resampling and reweighting, but …

Pioneering efforts have verified the effectiveness of the diffusion models in exploring the
informative uncertainty for recommendation. Considering the difference between …

Knowledge Graphs (KGs) have emerged as invaluable resources for enriching
recommendation systems by providing a wealth of factual information and capturing …

Sequential recommender systems often suffer from performance drops due to the data-
sparsity issue in real-world scenarios. To address this issue, we bravely take advantage of …

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