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Recommendation Systems

68 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

Training Deep AutoEncoders for Collaborative Filtering

5 Aug 2017NVIDIA/DeepRecommender

This paper proposes a novel model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.

COLLABORATIVE FILTERING

fastFM: A Library for Factorization Machines

4 May 2015ibayer/fastFM

Factorization Machines (FM) are only used in a narrow range of applications and are not part of the standard toolbox of machine learning models. This is a pity, because even though FMs are recognized as being very successful for recommender system type applications they are a general model to deal with sparse and high dimensional features.

RECOMMENDATION SYSTEMS

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

29 Oct 2018shenweichen/DeepCTR

The problem is very challenging since (1) the input features (e.g., the user id, user age, item id, item category) are usually sparse and high-dimensional, and (2) an effective prediction relies on high-order combinatorial features (a.k.a. Our proposed algorithm is very general, which can be applied to both numerical and categorical input features.

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. The data in user response prediction is mostly in a multi-field categorical format and transformed into sparse representations via one-hot encoding.

CLICK-THROUGH RATE PREDICTION 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. We conduct online A/B test in Huawei App Market, which reveals that DeepFM-D leads to more than 10% improvement of click-through rate in the production environment, compared to a well-engineered LR model.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems

14 Mar 2018shenweichen/DeepCTR

With the great success of deep neural networks (DNNs) in various fields, recently researchers have proposed several DNN-based factorization model to learn both low- and high-order feature interactions. 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. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering.

CLICK-THROUGH RATE PREDICTION 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. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding.

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. With less feature engineering, deep neural networks can generalize better to unseen feature combinations through low-dimensional dense embeddings learned for the sparse features.

CLICK-THROUGH RATE PREDICTION RECOMMENDATION SYSTEMS

Graph Convolutional Matrix Completion

7 Jun 2017tkipf/gae

We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings.

COLLABORATIVE FILTERING LINK PREDICTION MATRIX COMPLETION