215 citations · 227 across the 15 of their papers we have counts for
7 papers · 1 filter
An Efficient Framework for Whole-Page Reranking via Single-Modal Supervision
Zishuai Zhang, Sihao Yu, Wenyi Xie +5
The whole-page reranking plays a critical role in shaping the user experience of search engines, which integrates retrieval results from multiple modalities, such as documents, ima…
CTR-Guided Generative Query Suggestion in Conversational Search
Erxue Min, Hsiu-Yuan Huang, Xihong Yang +7
Generating effective query suggestions in conversational search requires aligning model outputs with user preferences, which is challenging due to sparse and noisy click signals. W…
Hgformer: Hyperbolic Graph Transformer for Recommendation
Xin Yang, Xingrun Li, Heng Chang +8
The cold start problem is a challenging problem faced by most modern recommender systems. By leveraging knowledge from other domains, cross-domain recommendation can be an effectiv…
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System
Xihong Yang, Heming Jing, Zixing Zhang +10
Benefiting from the strong reasoning capabilities, Large language models (LLMs) have demonstrated remarkable performance in recommender systems. Various efforts have been made to d…
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM
Xiaopeng Li, Lixin Su, Pengyue Jia +4
Search engines are crucial as they provide an efficient and easy way to access vast amounts of information on the internet for diverse information needs. User queries, even with a…
LLMRec: Large Language Models with Graph Augmentation for Recommendation
Wei Wei, Xubin Ren, Jiabin Tang +6
The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. Howev…
Representation Learning with Large Language Models for Recommendation
Xubin Ren, Wei Wei, Lianghao Xia +5
Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. How…