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Found 23 papers, 8 papers with code
Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
The data involved in CTR prediction are typically multi-field categorical data, i. e., every feature is categorical and belongs to one and only one field.
Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
Conversion prediction plays an important role in online advertising since Cost-Per-Action (CPA) has become one of the primary campaign performance objectives in the industry.
A Batched Multi-Armed Bandit Approach to News Headline Testing
At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a defined testing period, and then shift all future traffic to the best-performing variation.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.
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Click-Through Rate Prediction
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Bid Shading in The Brave New World of First-Price Auctions
Online auctions play a central role in online advertising, and are one of the main reasons for the industry's scalability and growth.
Bid Shading by Win-Rate Estimation and Surplus Maximization
We found that bid shading, in general, can deliver significant value to advertisers, reducing price per impression to about 55% of the unshaded cost.
Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
In e-commerce advertising, the ad platform usually relies on auction mechanisms to optimize different performance metrics, such as user experience, advertiser utility, and platform revenue.
$FM^2$: Field-matrixed Factorization Machines for Recommender Systems
The FmFM model's performance is also comparable to DNN models which require much more FLOPs in runtime.
An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions.
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 online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few minutes to several days.
Cross-Task Knowledge Distillation in Multi-Task Recommendation
Multi-task learning (MTL) has been widely used in recommender systems, wherein predicting each type of user feedback on items (e. g, click, purchase) are treated as individual tasks and jointly trained with a unified model.
Impression Allocation and Policy Search in Display Advertising
In order to address the unstable traffic pattern challenge and achieve the optimal overall outcome, we propose a multi-agent reinforcement learning method to adjust the bids from each guaranteed contract, which is simple, converging efficiently and scalable.
Trading Hard Negatives and True Negatives: A Debiased Contrastive Collaborative Filtering Approach
To circumvent false negatives, we develop a principled approach to improve the reliability of negative instances and prove that the objective is an unbiased estimation of sampling from the true negative distribution.
AutoAttention: Automatic Field Pair Selection for Attention in User Behavior Modeling
In this paper, we propose a novel model named AutoAttention, which includes all item/user/context side fields as the query, and assigns a learnable weight for each field pair between behavior fields and query fields.
AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning
In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter.
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Auxiliary-Task Learning (ATL) aims to improve the performance of the target task by leveraging the knowledge obtained from related tasks.
Temporal Interest Network for User Response Prediction
To fill this gap, we propose a Temporal Interest Network (TIN) to capture the semantic-temporal correlation simultaneously between behaviors and the target.
STEM: Unleashing the Power of Embeddings for Multi-task Recommendation
Surprisingly, negative transfer still occurs in existing MTL methods on samples that receive comparable feedback across tasks.
Decoupled Training: Return of Frustratingly Easy Multi-Domain Learning
Multi-domain learning (MDL) aims to train a model with minimal average risk across multiple overlapping but non-identical domains.
On the Embedding Collapse when Scaling up Recommendation Models
Recent advances in deep foundation models have led to a promising trend of developing large recommendation models to leverage vast amounts of available data.
Ad Recommendation in a Collapsed and Entangled World
In this paper, we present an industry ad recommendation system, paying attention to the challenges and practices of learning appropriate representations.
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
In this paper, we uncover a new challenge associated with BCE loss in scenarios with sparse positive feedback, such as CTR prediction: the gradient vanishing for negative samples.
Deep Pattern Network for Click-Through Rate Prediction
These patterns harbor substantial potential to significantly enhance CTR prediction performance.
