Click-Through Rate Prediction

75 papers with code • 17 benchmarks • 4 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 )

Libraries

Use these libraries to find Click-Through Rate Prediction models and implementations
23 papers
6,044
22 papers
199
4 papers
13,150
See all 6 libraries.

Most implemented papers

Wide & Deep Learning for Recommender Systems

microsoft/recommenders 24 Jun 2016

Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

xue-pai/FuxiCTR 13 Mar 2017

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.

Deep Interest Network for Click-Through Rate Prediction

zhougr1993/DeepInterestNetwork 21 Jun 2017

In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are.

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

Leavingseason/xDeepFM 14 Mar 2018

On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.

Deep Interest Evolution Network for Click-Through Rate Prediction

mouna99/dien 11 Sep 2018

Easy-to-use, Modular and Extendible package of deep-learning based CTR models. DeepFM, DeepInterestNetwork(DIN), DeepInterestEvolutionNetwork(DIEN), DeepCrossNetwork(DCN), AttentionalFactorizationMachine(AFM), Neural Factorization Machine(NFM), AutoInt

Deep & Cross Network for Ad Click Predictions

shenweichen/DeepCTR 17 Aug 2017

Feature engineering has been the key to the success of many prediction models.

Product-based Neural Networks for User Response Prediction

Atomu2014/product-nets 1 Nov 2016

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising.

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

hwwang55/RippleNet 9 Mar 2018

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.

AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

DeepGraphLearning/RecommenderSystems 29 Oct 2018

Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.

DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems

tensorflow/recommenders 19 Aug 2020

Learning effective feature crosses is the key behind building recommender systems.