52 citations · 52 across the 5 of their papers we have counts for
6 papers
Conditional Memory Enhanced Item Representation for Generative Recommendation
Ziwei Liu, Yejing Wang, Shengyu Zhou +2
Generative recommendation (GR) has emerged as a promising paradigm that predicts target items by autoregressively generating their semantic identifiers (SID). Most GR methods follo…
ChartAnchor: Chart Grounding with Structural-Semantic Fidelity
Xinhang Li, Jingbo Zhou, Pengfei Luo +2
Recent advances in multimodal large language models (MLLMs) highlight the need for benchmarks that rigorously evaluate structured chart comprehension. Chart grounding refers to the…
Empowering Large Language Model for Sequential Recommendation via Multimodal Embeddings and Semantic IDs
Yuhao Wang, Junwei Pan, Xinhang Li +6
Sequential recommendation (SR) aims to capture users' dynamic interests and sequential patterns based on their historical interactions. Recently, the powerful capabilities of large…
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu +9
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scen…
Large Language Model as Universal Retriever in Industrial-Scale Recommender System
Junguang Jiang, Yanwen Huang, Bin Liu +6
In real-world recommender systems, different retrieval objectives are typically addressed using task-specific datasets with carefully designed model architectures. We demonstrate t…
Contrastive Graph Pooling for Explainable Classification of Brain Networks
Jiaxing Xu, Qingtian Bian, Xinhang Li +6
Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying ne…