Click-Through Rate Prediction
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Benchmarks
Criteo
Avazu
Company*
Bing News
iPinYou
KKBox
MovieLens 1M
MovieLens 20M
Amazon
Dianping
Frappe
KDD12
MovieLens
Avito
Book-Crossing
Last.FM
Amazon Dataset
Android Malware Dataset
Huawei App Store
Most implemented
Wide & Deep Learning for Recommender Systems
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
Papers
Self-Balancing Gradient Allocation for Heterogeneity-Aware Feature Generation in Click-Through Rate Prediction
Generative pre-training via discrete diffusion provides dense reconstruction supervision across all feature fields simultaneously, mitigating representation collapse from data sparsity in CTR prediction. However, all exi…
Click-Through Rate PredictionSelective Test-Time Compute Scaling for Click-Through Rate Prediction via Uncertainty-Triggered Feature Path Exploration
Scaling test-time compute has proven highly effective for language models, yet this opportunity remains largely unexplored for industrial Click-Through Rate (CTR) prediction. CTR models suffer from a fundamental asymmetr…
Click-Through Rate PredictionGenerative Long-term User Interest Modeling for Click-Through Rate Prediction
Modeling long-term user interests with massive historical user behaviors enhances click-through rate (CTR) prediction performance in advertising and recommendation systems. Typically, a two-stage framework is widely adop…
Click-Through Rate PredictionRecommendation SystemsFEDIN: Frequency-Enhanced Deep Interest Network for Click-Through Rate Prediction
Sequential recommendation models often struggle to capture latent periodic patterns in user interests, primarily due to the noise inherent in time-domain behavioral data. While frequency-domain analysis offers a global p…
Click-Through Rate PredictionSequential RecommendationMixture of Sequence: Theme-Aware Mixture-of-Experts for Long-Sequence Recommendation
Sequential recommendation has rapidly advanced in click-through rate prediction due to its ability to model dynamic user interests. A key challenge, however, lies in modeling long sequences: users often exhibit significa…
Click-Through Rate PredictionSequential RecommendationtensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking values from a finite set. These attributes are often referred to as fields, and t…
Click-Through Rate Prediction