paper.dinq.meAI 与前沿科学顶会 · 学术新星雷达
登录 DINQ
返回新星榜
JL

Jiahao Liu

KDD 2026×2SIGIR 2026×2检索与知识数据与预训练对齐与偏好多模态效率与系统
新星分新星
85
一作贡献40
核心/参与44
方向广度13
合作网络6
1 一作 · 4 篇 · KDD 2026 / SIGIR 2026

获取完整研究档案

首次获取该候选人的完整档案及 StartLux 评估共 20 积分;每位用户单独解锁,之后查看免费。主动刷新 20 积分。

联系方式

实时查找电子邮箱,费用不超过20积分;未找到邮箱不扣费。

2026 顶会论文

4
作者位次 #2/8SIGIR 2026Oral· Information Retrieval (Full)

Drift-Aware Incremental Token Adaptation with Collaborative Semantics for Generative Recommendation

Yuebo Feng · Jiahao Liu · Mingzhe Han · Dongsheng Li · Hansu Gu · Peng Zhang · Tun Lu · Ning Gu

摘要

Generative recommendation commonly adopts a two-stage pipeline in which a learnable tokenizer maps items to discrete token sequences (i.e., identifiers) and an autoregressive generative recommender model (GRM) performs prediction based on these identifiers. Recent tokenizers further incorporate c...

数据与预训练检索与知识对齐与偏好OpenReview
作者位次 #2/10SIGIR 2026Poster· Information Retrieval (Short)

RQ-GMM: Residual Quantized Gaussian Mixture Model for Multimodal Semantic Discretization in CTR Prediction

Ziye Tong · Jiahao Liu · Weimin Zhang · Hongji Ruan · Derick Tang · Zhanpeng Zeng · Qinsong Zeng · Peng Zhang · Tun Lu · Ning Gu

摘要

Multimodal content is crucial for click-through rate (CTR) prediction. However, directly incorporating continuous embeddings from pre-trained models into CTR models yields suboptimal results due to misaligned optimization objectives and convergence speed inconsistency during joint training. Discr...

多模态数据与预训练对齐与偏好OpenReview

常见合作者