research
∙
09/19/2023
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Multi-domain learning (MDL) aims to train a model with minimal average r...
share
research
∙
08/16/2023
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Multi-task learning (MTL) has gained significant popularity in recommend...
research
∙
01/30/2023
ForkMerge: Overcoming Negative Transfer in Multi-Task Learning
The goal of multi-task learning is to utilize useful knowledge from mult...
research
∙
11/28/2022
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
Multi-task learning (MTL) models have demonstrated impressive results in...
research
∙
10/27/2022
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
In Click-through rate (CTR) prediction models, a user's interest is usua...
research
∙
04/25/2022
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
Collaborative filtering (CF), as a standard method for recommendation wi...
research
∙
03/11/2022
Impression Allocation and Policy Search in Display Advertising
In online display advertising, guaranteed contracts and real-time biddin...
research
∙
02/20/2022
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Multi-task learning (MTL) has been widely used in recommender systems, w...
research
∙
08/13/2021
Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback
The delayed feedback problem is one of the imperative challenges in onli...
research
∙
07/12/2021
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the onl...
research
∙
02/20/2021
FM^2: Field-matrixed Factorization Machines for Recommender Systems
Click-through rate (CTR) prediction plays a critical role in recommender...
research
∙
12/05/2020
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mec...
research
∙
09/19/2020
Bid Shading by Win-Rate Estimation and Surplus Maximization
This paper describes a new win-rate based bid shading algorithm (WR) tha...
research
∙
09/02/2020
Bid Shading in The Brave New World of First-Price Auctions
Online auctions play a central role in online advertising, and are one o...
research
∙
02/17/2020
A Sparse Deep Factorization Machine for Efficient CTR prediction
Click-through rate (CTR) prediction is a crucial task in online display ...
research
∙
08/17/2019
A Batched Multi-Armed Bandit Approach to News Headline Testing
Optimizing news headlines is important for publishers and media sites. A...
research
∙
07/24/2019
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
Conversion prediction plays an important role in online advertising sinc...
research
∙
06/09/2018
