21 citations · 34 across the 17 of their papers we have counts for
16 papers · 1 filter
Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
Wenlin Zhang, Xiangyang Li, Qiyuan Ge +9
In recommender systems, online A/B testing is a crucial method for evaluating the performance of different models. However, conducting online A/B testing often presents significant…
MTRec: Learning to Align with User Preferences via Mental Reward Models
Mengchen Zhao, Yifan Gao, Yaqing Hou +5
Recommendation models are predominantly trained using implicit user feedback, since explicit feedback is often costly to obtain. However, implicit feedback, such as clicks, does no…
Process vs. Outcome Reward: Which is Better for Agentic RAG Reinforcement Learning
Wenlin Zhang, Xiangyang Li, Kuicai Dong +9
Retrieval-augmented generation (RAG) enhances the text generation capabilities of large language models (LLMs) by integrating external knowledge and up-to-date information. However…
A Survey of Personalization: From RAG to Agent
Xiaopeng Li, Pengyue Jia, Derong Xu +11
Personalization has become an essential capability in modern AI systems, enabling customized interactions that align with individual user preferences, contexts, and goals. Recent r…
SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
Jingtong Gao, Zhaocheng Du, Xiaopeng Li +5
Tabular data synthesis is crucial in machine learning, yet existing general methods-primarily based on statistical or deep learning models-are highly data-dependent and often fall…
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…
LIBER: Lifelong User Behavior Modeling Based on Large Language Models
Chenxu Zhu, Shigang Quan, Bo Chen +7
CTR prediction plays a vital role in recommender systems. Recently, large language models (LLMs) have been applied in recommender systems due to their emergence abilities. While le…
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…
CELA: Cost-Efficient Language Model Alignment for CTR Prediction
Xingmei Wang, Weiwen Liu, Xiaolong Chen +8
Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed dist…
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,…
A Unified Framework for Multi-Domain CTR Prediction via Large Language Models
Zichuan Fu, Xiangyang Li, Chuhan Wu +6
Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items b…
FLIP: Fine-grained Alignment between ID-based Models and Pretrained Language Models for CTR Prediction
Hangyu Wang, Jianghao Lin, Xiangyang Li +5
Click-through rate (CTR) prediction plays as a core function module in various personalized online services. The traditional ID-based models for CTR prediction take as inputs the o…
How Can Recommender Systems Benefit from Large Language Models: A Survey
Jianghao Lin, Xinyi Dai, Yunjia Xi +11
With the rapid development of online services, recommender systems (RS) have become increasingly indispensable for mitigating information overload. Despite remarkable progress, con…
CTRL: Connect Collaborative and Language Model for CTR Prediction
Xiangyang Li, Bo Chen, Lu Hou +1
Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot vectors and leverage the collaborative relations among features for inferring the user'…