activity
20182025
most citedQuery-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU

2 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2025

Equip Pre-ranking with Target Attention by Residual Quantization

Yutong Li, Yu Zhu, Yichen Qiao +4

The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful models like Target Attention (TA) excel…

cs.IR2020★ 1 cited

Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

Runsheng Yu, Yu Gong, Xu He +4

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data c…

cs.IR2019★ 2 cited

Query-based Interactive Recommendation by Meta-Path and Adapted Attention-GRU

Yu Zhu, Yu Gong, Qingwen Liu +6

Recently, interactive recommender systems are becoming increasingly popular. The insight is that, with the interaction between users and the system, (1) users can actively interven…

cs.IR2019★ 2 cited

Exact-K Recommendation via Maximal Clique Optimization

Yu Gong, Yu Zhu, Lu Duan +5

This paper targets to a novel but practical recommendation problem named exact-K recommendation. It is different from traditional top-K recommendation, as it focuses more on (const…

cs.CL2018★ 1 cited

Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant

Yu Gong, Xusheng Luo, Yu Zhu +6

Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such model…