4 citations · 5 across the 6 of their papers we have counts for
7 papers · 1 filter
Exploring Preference-Guided Diffusion Model for Cross-Domain Recommendation
Xiaodong Li, Hengzhu Tang, Jiawei Sheng +5
Cross-domain recommendation (CDR) has been proven as a promising way to alleviate the cold-start issue, in which the most critical problem is how to draw an informative user repres…
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…
Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
Qian Dong, Yiding Liu, Suqi Cheng +4
Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their ov…
Pre-trained Language Model based Ranking in Baidu Search
Lixin Zou, Shengqiang Zhang, Hengyi Cai +8
As the heart of a search engine, the ranking system plays a crucial role in satisfying users' information demands. More recently, neural rankers fine-tuned from pre-trained languag…