activity
20232026
most citedHAMUR: Hyper Adapter for Multi-Domain Recommendation

51 citations · 56 across the 18 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.IR2026

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…

cs.IR2026

Reinforced Preference Optimization for Reasoning-Augmented Recommendations

Jingtong Gao, Zeyu Song, Chi Lu +7

Recommender systems are critical for delivering personalized content across digital platforms, and recent advances in Large Language Models (LLMs) offer new opportunities to enhanc…

cs.IR2026

To Search or Not to Search: Aligning the Decision Boundary of Deep Search Agents via Causal Intervention

Wenlin Zhang, Kuicai Dong, Junyi Li +9

Deep search agents, which autonomously iterate through multi-turn web-based reasoning, represent a promising paradigm for complex information-seeking tasks. However, current agents…

cs.CL2026

Enhancing Conversational Agents via Task-Oriented Adversarial Memory Adaptation

Yimin Deng, Yuqing Fu, Derong Xu +10

Conversational agents struggle to handle long conversations due to context window limitations. Therefore, memory systems are developed to leverage essential historical information.…

cs.IR2026

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…