most citedTowards Explainable Collaborative Filtering with Taste Clusters Learning

9 citations · 15 across the 4 of their papers we have counts for

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cs.IR2026

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…

cs.IR2026

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…

cs.IR2023★ 9 cited

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…