747 citations · 750 across the 9 of their papers we have counts for
10 papers
UniSGR-V2: Stepwise Target-Side Feature Anticipation and Crossing for Generative Recommendation
Jun Yang, Jiasi Chen, Jiawei Sun +4
Semantic-ID-based generative recommendation unifies retrieval and ranking through autoregressive decoding but differs structurally from discriminative models, which can directly ac…
UniSGR: Unified Framework for Semantic ID Generation and Ranking
Jiawei Sun, Jun Yang, Ziyue Guo +4
Recommendation systems play a pivotal role in modern e-commerce platforms. While generative retrieval has emerged as a promising paradigm for alleviating the limitations of multi-s…
GPR: Towards a Generative Pre-trained One-Model Paradigm for Large-Scale Advertising Recommendation
Jun Zhang, Yi Li, Yue Liu +19
As an intelligent infrastructure connecting users with commercial content, advertising recommendation systems play a central role in information flow and value creation within the…
Theoretical Learning Performance of Graph Neural Networks: The Impact of Jumping Connections and Layer-wise Sparsification
Jiawei Sun, Hongkang Li, Meng Wang
Jumping connections enable Graph Convolutional Networks (GCNs) to overcome over-smoothing, while graph sparsification reduces computational demands by selecting a sub-matrix of the…
Benchmarking Large Language Models on Multiple Tasks in Bioinformatics NLP with Prompting
Jiyue Jiang, Pengan Chen, Jiuming Wang +13
Large language models (LLMs) have become important tools in solving biological problems, offering improvements in accuracy and adaptability over conventional methods. Several bench…
LocalGCL: Local-aware Contrastive Learning for Graphs
Haojun Jiang, Jiawei Sun, Jie Li +1
Graph representation learning (GRL) makes considerable progress recently, which encodes graphs with topological structures into low-dimensional embeddings. Meanwhile, the time-cons…
GSINA: Improving Subgraph Extraction for Graph Invariant Learning via Graph Sinkhorn Attention
Junchi Yan, Fangyu Ding, Jiawei Sun +3
Graph invariant learning (GIL) seeks invariant relations between graphs and labels under distribution shifts. Recent works try to extract an invariant subgraph to improve out-of-di…
Retrieval-Augmented Generation for Large Language Models: A Survey
Yunfan Gao, Yun Xiong, Xinyu Gao +7
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes.…
Flames: Benchmarking Value Alignment of LLMs in Chinese
Kexin Huang, Xiangyang Liu, Qianyu Guo +9
The widespread adoption of large language models (LLMs) across various regions underscores the urgent need to evaluate their alignment with human values. Current benchmarks, howeve…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…