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Found 10 papers, 8 papers with code
FedUD: Exploiting Unaligned Data for Cross-Platform Federated Click-Through Rate Prediction
In the second step, FedUD applies the learned knowledge to enrich the representations of the host party's unaligned data such that both aligned and unaligned data can contribute to federated model training.
Masked Multi-Domain Network: Multi-Type and Multi-Scenario Conversion Rate Prediction with a Single Model
A desired model for this problem should satisfy the following requirements: 1) Accuracy: the model should achieve fine-grained accuracy with respect to any conversion type in any display scenario.
Contrastive Learning for Conversion Rate Prediction
To tailor the contrastive learning task to the CVR prediction problem, we propose embedding masking (EM), rather than feature masking, to create two views of augmented samples.
Learning Graph Meta Embeddings for Cold-Start Ads in Click-Through Rate Prediction
For existing old ads, GMEs first build a graph to connect them with new ads, and then adaptively distill useful information.
MiNet: Mixed Interest Network for Cross-Domain Click-Through Rate Prediction
Our study is based on UC Toutiao (a news feed service integrated with the UC Browser App, serving hundreds of millions of users daily), where the source domain is the news and the target domain is the ad.
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection
Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.
Click-Through Rate Prediction with the User Memory Network
Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services.
Representation Learning-Assisted Click-Through Rate Prediction
These subnets model the user-ad, ad-ad and feature-CTR relationship respectively.
Ranked #2 on
Click-Through Rate Prediction
on Avito
Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction
The intuitions are that ads shown together may influence each other, clicked ads reflect a user's preferences, and unclicked ads may indicate what a user dislikes to certain extent.
Ranked #1 on
Click-Through Rate Prediction
on Avito
DeepHawkes: Bridging the gap between prediction and understanding of information cascades
In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.
