21 citations
- Ansys (United States)US1 paper
- Carnegie Mellon UniversityUS1 paper
- LinkedIn (United States)US1 paper
- Microsoft Research (India)IN1 paper
- National University of SingaporeSG1 paper
- The Ohio State UniversityUS1 paper
- The University of Texas at AustinUS1 paper
- University of California, BerkeleyUS1 paper
- University of California, Los AngelesUS1 paper
- University of Science and Technology of ChinaCN1 paper
- Yahoo (United States)US1 paper
8 papers
Reformulating CTR Prediction: Learning Invariant Feature Interactions for Recommendation
Yang Zhang, Tianhao Shi, Fuli Feng +4
Click-Through Rate (CTR) prediction plays a core role in recommender systems, serving as the final-stage filter to rank items for a user. The key to addressing the CTR task is lear…
Causal Meta-Mediation Analysis: Inferring Dose-Response Function From Summary Statistics of Many Randomized Experiments
Zenan Wang, Xuan Yin, Tianbo Li +1
It is common in the internet industry to use offline-developed algorithms to power online products that contribute to the success of a business. Offline-developed algorithms are gu…
The Identification and Estimation of Direct and Indirect Effects in A/B Tests through Causal Mediation Analysis
Xuan Yin, Liangjie Hong
E-commerce companies have a number of online products, such as organic search, sponsored search, and recommendation modules, to fulfill customer needs. Although each of these produ…
An Ensemble-based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy
Kamelia Aryafar, Devin Guillory, Liangjie Hong
Etsy is a global marketplace where people across the world connect to make, buy and sell unique goods. Sellers at Etsy can promote their product listings via advertising campaigns…
On Sampling Strategies for Neural Network-based Collaborative Filtering
Ting Chen, Yizhou Sun, Yue Shi +1
Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content i…
Images Don't Lie: Transferring Deep Visual Semantic Features to Large-Scale Multimodal Learning to Rank
Corey Lynch, Kamelia Aryafar, Josh Attenberg
Search is at the heart of modern e-commerce. As a result, the task of ranking search results automatically (learning to rank) is a multibillion dollar machine learning problem. Tra…