31 citations · 35 across the 3 of their papers we have counts for
3 papers
cs.IR2022★ 4 cited
Improving Multi-Interest Network with Stable Learning
Zhaocheng Liu, Yingtao Luo, Di Zeng +4
Modeling users' dynamic preferences from historical behaviors lies at the core of modern recommender systems. Due to the diverse nature of user interests, recent advances propose t…
cs.IR2021
Context-aware Tree-based Deep Model for Recommender Systems
Daqing Chang, Jintao Liu, Ziru Xu +3
How to predict precise user preference and how to make efficient retrieval from a big corpus are two major challenges of large-scale industrial recommender systems. In tree-based m…
cs.IR2019★ 31 cited
Joint Optimization of Tree-based Index and Deep Model for Recommender Systems
Han Zhu, Daqing Chang, Ziru Xu +6
Large-scale industrial recommender systems are usually confronted with computational problems due to the enormous corpus size. To retrieve and recommend the most relevant items to…