213 citations · 706 across the 25 of their papers we have counts for
8 papers · 1 filter
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…
Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising
Xiangyu Liu, Chuan Yu, Zhilin Zhang +10
In e-commerce advertising, it is crucial to jointly consider various performance metrics, e.g., user experience, advertiser utility, and platform revenue. Traditional auction mecha…
We Know What You Want: An Advertising Strategy Recommender System for Online Advertising
Liyi Guo, Junqi Jin, Haoqi Zhang +10
Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial a…
A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising
Chao Wen, Miao Xu, Zhilin Zhang +12
In online advertising, auto-bidding has become an essential tool for advertisers to optimize their preferred ad performance metrics by simply expressing high-level campaign objecti…
Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling
Siyu Gu, Xiang-Rong Sheng, Ying Fan +2
One of the difficulties of conversion rate (CVR) prediction is that the conversions can delay and take place long after the clicks. The delayed feedback poses a challenge: fresh da…
Computation Resource Allocation Solution in Recommender Systems
Xun Yang, Yunli Wang, Cheng Chen +4
Recommender systems rely heavily on increasing computation resources to improve their business goal. By deploying computation-intensive models and algorithms, these systems are abl…
Truncation-Free Matching System for Display Advertising at Alibaba
Jin Li, Jie Liu, Shangzhou Li +8
Matching module plays a critical role in display advertising systems. Without query from user, it is challenging for system to match user traffic and ads suitably. System packs up…
One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction
Xiang-Rong Sheng, Liqin Zhao, Guorui Zhou +8
Traditional industrial recommenders are usually trained on a single business domain and then serve for this domain. However, in large commercial platforms, it is often the case tha…