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From the 1 of 40 linked papers with an AI index.

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20242026
most citedCurriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning

1 citations · 1 across the 14 of their papers we have counts for

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cs.IR2025

DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management

Kai Yin, Xiangjue Dong, Chengkai Liu +4

Effective and efficient access to relevant information is essential for disaster management. However, no retrieval model is specialized for disaster management, and existing genera…

cs.IR2025

DisastIR: A Comprehensive Information Retrieval Benchmark for Disaster Management

Kai Yin, Xiangjue Dong, Chengkai Liu +5

Effective disaster management requires timely access to accurate and contextually relevant information. Existing Information Retrieval (IR) benchmarks, however, focus primarily on…

cs.IR2025

Flow Matching for Collaborative Filtering

Chengkai Liu, Yangtian Zhang, Jianling Wang +2

Generative models have shown great promise in collaborative filtering by capturing the underlying distribution of user interests and preferences. However, existing approaches strug…

cs.IR2025

Towards An Efficient LLM Training Paradigm for CTR Prediction

Allen Lin, Renqin Cai, Yun He +5

Large Language Models (LLMs) have demonstrated tremendous potential as the next-generation ranking-based recommendation system. Many recent works have shown that LLMs can significa…

cs.IR2025

Federated Conversational Recommender System

Allen Lin, Jianling Wang, Ziwei Zhu +1

Conversational Recommender Systems (CRSs) have become increasingly popular as a powerful tool for providing personalized recommendation experiences. By directly engaging with users…

cs.IR2025

Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap

Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15

Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…