51 citations · 71 across the 40 of their papers we have counts for
10 papers · 1 filter
SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval
Xiaopeng Li, Xiangyang Li, Hao Zhang +6
The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniqu…
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation
Pengyue Jia, Derong Xu, Xiaopeng Li +9
The reranker and generator are two critical components in the Retrieval-Augmented Generation (i.e., RAG) pipeline, responsible for ranking relevant documents and generating respons…
SELF: Surrogate-light Feature Selection with Large Language Models in Deep Recommender Systems
Pengyue Jia, Zhaocheng Du, Yichao Wang +6
Feature selection is crucial in recommender systems for improving model efficiency and predictive performance. Conventional approaches typically employ surrogate models-such as dec…
Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models
Yuhao Wang, Junwei Pan, Pengyue Jia +6
Sequential Recommendation (SR) aims to leverage the sequential patterns in users' historical interactions to accurately track their preferences. However, the primary reliance of ex…
Prompt Tuning as User Inherent Profile Inference Machine
Yusheng Lu, Zhaocheng Du, Xiangyang Li +9
Large Language Models (LLMs) have exhibited significant promise in recommender systems by empowering user profiles with their extensive world knowledge and superior reasoning capab…
LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation
Yuhao Wang, Yichao Wang, Zichuan Fu +4
As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much att…
LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations
Jingtong Gao, Bo Chen, Weiwen Liu +6
Reranking is a critical component in recommender systems, playing an essential role in refining the output of recommendation algorithms. Traditional reranking models have focused p…
G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models
Pengyue Jia, Yiding Liu, Xiaopeng Li +7
Worldwide geolocalization aims to locate the precise location at the coordinate level of photos taken anywhere on the Earth. It is very challenging due to 1) the difficulty of capt…
LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
Wenlin Zhang, Chuhan Wu, Xiangyang Li +7
The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem,…
ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems
Pengyue Jia, Yejing Wang, Zhaocheng Du +6
Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently…