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Click-Through Rate Prediction

26 papers with code · Miscellaneous

Click-through rate prediction is the task of predicting the likelihood that something on a website (such as an advertisement) will be clicked.

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Latest papers with code

Mixed Dimension Embeddings with Application to Memory-Efficient Recommendation Systems

25 Sep 2019facebookresearch/dlrm

In this work, we propose mixed dimension embedding layers in which the dimension of a particular embedding vector can depend on the frequency of the item.

CLICK-THROUGH RATE PREDICTION COLLABORATIVE FILTERING

1,475
25 Sep 2019

An End-to-End Neighborhood-based Interaction Model forKnowledge-enhanced Recommendation

12 Aug 2019Atomu2014/KNI

This paper studies graph-based recommendation, where an interaction graph is constructed built from historical records and is lever-aged to alleviate data sparsity and cold start problems.

CLICK-THROUGH RATE PREDICTION KNOWLEDGE GRAPHS

20
12 Aug 2019

Click-Through Rate Prediction with the User Memory Network

9 Jul 2019rener1199/deep_memory

Both offline and online experiments demonstrate the effectiveness of MA-DNN for practical CTR prediction services.

CLICK-THROUGH RATE PREDICTION

31
09 Jul 2019

Representation Learning-Assisted Click-Through Rate Prediction

11 Jun 2019oywtece/deepmcp

These subnets model the user-ad, ad-ad and feature-CTR relationship respectively.

CLICK-THROUGH RATE PREDICTION REPRESENTATION LEARNING

62
11 Jun 2019

Deep Spatio-Temporal Neural Networks for Click-Through Rate Prediction

10 Jun 2019oywtece/dstn

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.

CLICK-THROUGH RATE PREDICTION

85
10 Jun 2019

FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction

23 May 2019shenweichen/DeepCTR

In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.

CLICK-THROUGH RATE PREDICTION FEATURE IMPORTANCE

2,141
23 May 2019

Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction

22 May 2019UIC-Paper/MIMN

To our knowledge, this is one of the first industrial solutions that are capable of handling long sequential user behavior data with length scaling up to thousands.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

34
22 May 2019

Deep Session Interest Network for Click-Through Rate Prediction

16 May 2019shenweichen/DeepCTR

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 Session Interest Network(DSIN)

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

2,141
16 May 2019

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

9 Apr 2019shenweichen/DeepCTR

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 Session Interest Network(DSIN)

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

2,141
09 Apr 2019

Structured Semantic Model supported Deep Neural Network for Click-Through Rate Prediction

4 Dec 2018niuchenglei/ssm-dnn

With the rapid development of online advertising and recommendation systems, click-through rate prediction is expected to play an increasingly important role. Recently many DNN-based models which follow a similar Embedding&MLP paradigm have been proposed, and have achieved good result in image/voice and nlp fields.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

7
04 Dec 2018