16 citations · 43 across the 8 of their papers we have counts for
8 papers
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint
Ziang Yan, Shusen Wang, Guorui Zhou +2
Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level bu…
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…
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…
CAN: Feature Co-Action for Click-Through Rate Prediction
Weijie Bian, Kailun Wu, Lejian Ren +12
Feature interaction has been recognized as an important problem in machine learning, which is also very essential for click-through rate (CTR) prediction tasks. In recent years, De…
COLD: Towards the Next Generation of Pre-Ranking System
Zhe Wang, Liqin Zhao, Biye Jiang +3
Multi-stage cascade architecture exists widely in many industrial systems such as recommender systems and online advertising, which often consists of sequential modules including m…
Search-based User Interest Modeling with Lifelong Sequential Behavior Data for Click-Through Rate Prediction
Pi Qi, Xiaoqiang Zhu, Guorui Zhou +5
Rich user behavior data has been proven to be of great value for click-through rate prediction tasks, especially in industrial applications such as recommender systems and online a…
DCAF: A Dynamic Computation Allocation Framework for Online Serving System
Biye Jiang, Pengye Zhang, Rihan Chen +7
Modern large-scale systems such as recommender system and online advertising system are built upon computation-intensive infrastructure. The typical objective in these applications…
A Deep Recurrent Survival Model for Unbiased Ranking
Jiarui Jin, Yuchen Fang, Weinan Zhang +7
Position bias is a critical problem in information retrieval when dealing with implicit yet biased user feedback data. Unbiased ranking methods typically rely on causality models a…