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Recommendation Systems

129 papers with code · Miscellaneous

The recommendation systems task is to produce a list of recommendations for a user.

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Greatest papers with code

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

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

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

29 Oct 2018shenweichen/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

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

Product-based Neural Networks for User Response Prediction over Multi-field Categorical Data

1 Jul 2018shenweichen/DeepCTR

User response prediction is a crucial component for personalized information retrieval and filtering scenarios, such as recommender system and web search.

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING INFORMATION RETRIEVAL RECOMMENDATION SYSTEMS

DeepFM: An End-to-End Wide & Deep Learning Framework for CTR Prediction

12 Apr 2018shenweichen/DeepCTR

In this paper, we study two instances of DeepFM where its "deep" component is DNN and PNN respectively, for which we denote as DeepFM-D and DeepFM-P. Comprehensive experiments are conducted to demonstrate the effectiveness of DeepFM-D and DeepFM-P over the existing models for CTR prediction, on both benchmark data and commercial data.

CLICK-THROUGH RATE PREDICTION FEATURE ENGINEERING RECOMMENDATION SYSTEMS

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

14 Mar 2018shenweichen/DeepCTR

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.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

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

Training Deep AutoEncoders for Collaborative Filtering

5 Aug 2017NVIDIA/DeepRecommender

Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.

COLLABORATIVE FILTERING