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Click-Through Rate Prediction

27 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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Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

18 Apr 2017shenweichen/DeepCTR

CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data.

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING

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

13 Mar 2017shenweichen/DeepCTR

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

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING RECOMMENDATION SYSTEMS

Product-based Neural Networks for User Response Prediction

1 Nov 2016shenweichen/DeepCTR

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.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

Wide & Deep Learning for Recommender Systems

24 Jun 2016shenweichen/DeepCTR

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

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING RECOMMENDATION SYSTEMS REGRESSION

Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction

11 Jan 2016shenweichen/DeepCTR

Different from continuous raw features that we usually found in the image and audio domains, the input features in web space are always of multi-field and are mostly discrete and categorical while their dependencies are little known.

CLICK-THROUGH RATE PREDICTION

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

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

9 Mar 2018hwwang55/RippleNet

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.

CLICK-THROUGH RATE PREDICTION COLLABORATIVE FILTERING

DKN: Deep Knowledge-Aware Network for News Recommendation

25 Jan 2018hwwang55/DKN

To solve the above problems, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation.

CLICK-THROUGH RATE PREDICTION COMMON SENSE REASONING RECOMMENDATION SYSTEMS

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

Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

14 Aug 2017zhougr1993/Rocket-Launching

Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time.

CLICK-THROUGH RATE PREDICTION