Click-Through Rate Prediction
135 papers with code • 19 benchmarks • 7 datasets
Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.
( Image credit: Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction )
Benchmarks
These leaderboards are used to track progress in Click-Through Rate Prediction
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Libraries
Use these libraries to find Click-Through Rate Prediction models and implementations
MovieLens
CBT
Criteo
iPinYou
TripClick
KDD12
MerRec
Latest papers with no code
Deep Pattern Network for Click-Through Rate Prediction
These patterns harbor substantial potential to significantly enhance CTR prediction performance.
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Moreover, we adopt a two-stage learning strategy for skill-aware recommendation, in which we utilize the skill distribution to guide JD representation learning in the recall stage, and then combine the user profiles for final prediction in the ranking stage.
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency of LLMs when processing long textual user behaviors.
PPM : A Pre-trained Plug-in Model for Click-through Rate Prediction
However, the explosive growth of online latency can be attributed to the huge parameters in the pre-trained model.
MetaSplit: Meta-Split Network for Limited-Stock Product Recommendation
Due to limited user interactions for each product (i. e. item), the corresponding item embedding in the CTR model may not easily converge.
Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate and charge the price of the winner through a pay-per-click manner.
LiRank: Industrial Large Scale Ranking Models at LinkedIn
We present LiRank, a large-scale ranking framework at LinkedIn that brings to production state-of-the-art modeling architectures and optimization methods.
Calibration-then-Calculation: A Variance Reduced Metric Framework in Deep Click-Through Rate Prediction Models
The metric variance comes from the randomness inherent in the training process of deep learning pipelines.
GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
In this paper, we proposed GACE, a graph-based cross-page ads embedding generation method.
