activity
20172026
most citedLearning Tree-based Deep Model for Recommender Systems

301 citations · 718 across the 46 of their papers we have counts for

collaborators
Showing 2020Show all

5 papers · 1 filter

cs.IR2020★ 11 cited

Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning

Chao Du, Zhifeng Gao, Shuo Yuan +7

Modern online advertising systems inevitably rely on personalization methods, such as click-through rate (CTR) prediction. Recent progress in CTR prediction enjoys the rich represe…

cs.AI2020★ 1 cited

Learning to Infer User Hidden States for Online Sequential Advertising

Zhaoqing Peng, Junqi Jin, Lan Luo +11

To drive purchase in online advertising, it is of the advertiser's great interest to optimize the sequential advertising strategy whose performance and interpretability are both im…

cs.SI2020★ 11 cited

A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction

Liyi Guo, Rui Lu, Haoqi Zhang +9

For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and sho…

cs.LG2020★ 9 cited

Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising

Xiaotian Hao, Zhaoqing Peng, Yi Ma +12

In E-commerce, advertising is essential for merchants to reach their target users. The typical objective is to maximize the advertiser's cumulative revenue over a period of time un…

stat.ML2020★ 7 cited

Learning Optimal Tree Models Under Beam Search

Jingwei Zhuo, Ziru Xu, Wei Dai +4

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, w…