9 citations · 16 across the 5 of their papers we have counts for
5 papers
RecHarness: A Bandit-Routed Agentic Harness for Self-Evolving Recommender Systems
Haoran Ling, Yuecheng Li, Zeyu Song +5
Optimizing modern recommender models still depends heavily on engineers manually iterating over architectural, objective, and training-strategy changes. While LLM-based agents can…
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
Yuecheng Li, Zeyu Song, Jing Yao +3
Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry. However, aligning the LLM's semantic space with the recommender's ID spac…
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Jing Yao, Xiaoyuan Yi, Xiting Wang +2
Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly…
Towards Explainable Collaborative Filtering with Taste Clusters Learning
Yuntao Du, Jianxun Lian, Jing Yao +5
Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based…
UA-FedRec: Untargeted Attack on Federated News Recommendation
Jingwei Yi, Fangzhao Wu, Bin Zhu +4
News recommendation is critical for personalized news distribution. Federated news recommendation enables collaborative model learning from many clients without sharing their raw d…