19 papers
Hierarchical Quantization with Domain-Adaptive Sparse Routing for Generative Cross-Domain Recommendation
Haiying He, Xiaopeng Li, Yuchen Gu +9
Generative Recommendation (GenRec) represents a promising paradigm that achieves remarkable empirical success by encoding items as compact Semantic IDs (SIDs) and modeling user beh…
Align-GRAG: Anchor and Rationale Guided Dual Alignment for Graph Retrieval-Augmented Generation
Derong Xu, Pengyue Jia, Xiaopeng Li +9
Despite the strong abilities, large language models (LLMs) still suffer from hallucinations and reliance on outdated knowledge, raising concerns in knowledge-intensive tasks. Graph…
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…
No One Left Behind: How to Exploit the Incomplete and Skewed Multi-Label Data for Conversion Rate Prediction
Qinglin Jia, Zhaocheng Du, Chuhan Wu +4
In most real-world online advertising systems, advertisers typically have diverse customer acquisition goals. A common solution is to use multi-task learning (MTL) to train a unifi…
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…
LSRP: A Leader-Subordinate Retrieval Framework for Privacy-Preserving Cloud-Device Collaboration
Yingyi Zhang, Pengyue Jia, Xianneng Li +8
Cloud-device collaboration leverages on-cloud Large Language Models (LLMs) for handling public user queries and on-device Small Language Models (SLMs) for processing private user d…