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Found 8 papers, 4 papers with code
Making the Full Model Adaptive: Multi-level Domain Adaptation for Multi-Domain CTR Prediction
We then select them in a domain-aware way to promote informative features for different domains.
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
To directly introduce the correct feedback label information, we propose an Unbiased delayed feedback Label Correction framework (ULC), which uses an auxiliary model to correct labels for observed negative feedback samples.
Contrastive Multi-view Framework for Customer Lifetime Value Prediction
Accurate customer lifetime value (LTV) prediction can help service providers optimize their marketing policies in customer-centric applications.
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
FE-Block module performs fine-grained and early feature interactions to capture the interactive signals between user and item towers explicitly and CIR module leverages a contrastive interaction regularization to further enhance the interactions implicitly.
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation
Learning vectorized embeddings is at the core of various recommender systems for user-item matching.
Towards Low-loss 1-bit Quantization of User-item Representations for Top-K Recommendation
In this work, we study the problem of representation learning for recommendation with 1-bit quantization.
Why Do We Click: Visual Impression-aware News Recommendation
In this work, inspired by the fact that users make their click decisions mostly based on the visual impression they perceive when browsing news, we propose to capture such visual impression information with visual-semantic modeling for news recommendation.
UNBERT: User-News Matching BERT for News Recommendation
Nowadays, news recommendation has become a popular channel for users to access news of their interests.
