4 papers
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…
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
Huanshuo Liu, Bo Chen, Menghui Zhu +5
Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However…
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Bo Chen, Xinyi Dai, Huifeng Guo +9
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large lan…
Evaluating the External and Parametric Knowledge Fusion of Large Language Models
Hao Zhang, Yuyang Zhang, Xiaoguang Li +8
Integrating external knowledge into large language models (LLMs) presents a promising solution to overcome the limitations imposed by their antiquated and static parametric memory.…