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Found 7 papers, 7 papers with code
Federated Adaptation for Foundation Model-based Recommendations
It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy.
End-to-end training of Multimodal Model and ranking Model
In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.
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.
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
