51 citations · 95 across the 8 of their papers we have counts for
6 papers · 1 filter
Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation
Jingtong Gao, Bo Chen, Menghui Zhu +6
Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations…
HAMUR: Hyper Adapter for Multi-Domain Recommendation
Xiaopeng Li, Fan Yan, Xiangyu Zhao +4
Multi-Domain Recommendation (MDR) has gained significant attention in recent years, which leverages data from multiple domains to enhance their performance concurrently.However, cu…
Multi-Task Deep Recommender Systems: A Survey
Yuhao Wang, Ha Tsz Lam, Yi Wong +8
Multi-task learning (MTL) aims at learning related tasks in a unified model to achieve mutual improvement among tasks considering their shared knowledge. It is an important topic i…
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
Xiangyang Li, Bo Chen, HuiFeng Guo +10
Scoring a large number of candidates precisely in several milliseconds is vital for industrial pre-ranking systems. Existing pre-ranking systems primarily adopt the \textbf{two-tow…
A Practical Incremental Method to Train Deep CTR Models
Yichao Wang, Huifeng Guo, Ruiming Tang +2
Deep learning models in recommender systems are usually trained in the batch mode, namely iteratively trained on a fixed-size window of training data. Such batch mode training of d…
Personalized Re-ranking for Improving Diversity in Live Recommender Systems
Yichao Wang, Xiangyu Zhang, Zhirong Liu +4
Users of industrial recommender systems are normally suggesteda list of items at one time. Ideally, such list-wise recommendationshould provide diverse and relevant options to the…