miscellaneous-5

Collaborative Filtering

3260 papers • 126 benchmarks • 313 datasets

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massquantity/LibRecommender

4 papers 302

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Most implemented papers

Neural Collaborative Filtering

Xiangnan He, Tat-Seng Chua, Lizi Liao, Liqiang Nie, Hanwang Zhang, Xia Hu•Sun Apr 02 2017

This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering.

6729

Content

nzhinusoftcm/review-on-collaborativ…

3 papers 25

shenweichen/deepmatch

2 papers 2,123

xue-pai/FuxiCTR

2 papers 740

hkuds/sslrec

2 papers 262

Evfro/polara

2 papers 247

lmcRS/AWS-recommendation-papers

2 papers 55

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Paper Graph

Neural Graph Collaborative Filtering

Xiangnan He, Tat-Seng Chua, Fuli Feng, Xiang Wang, Meng Wang•Sun May 19 2019

This work develops a new recommendation framework Neural Graph Collaborative Filtering (NGCF), which exploits the user-item graph structure by propagating embeddings on it, effectively injecting the collaborative signal into the embedding process in an explicit manner.

3525 0

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Variational Autoencoders for Collaborative Filtering

M. Hoffman, R. G. Krishnan, Dawen Liang, Tony Jebara•Thu Feb 15 2018

A generative model with multinomial likelihood and use Bayesian inference for parameter estimation is introduced and the pros and cons of employing a principledBayesian inference approach are identified and characterize settings where it provides the most significant improvements.

1406 0

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LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

Xiangnan He, Yongdong Zhang, Xiang Wang, Kuan Deng, Yan Li, Meng Wang•Wed Feb 05 2020

This work proposes a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering, and is much easier to implement and train, exhibiting substantial improvements over Neural Graph Collaborative Filtering (NGCF) under exactly the same experimental setting.

4681 0

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A contextual-bandit approach to personalized news article recommendation

J. Langford, Lihong Li, Wei Chu, R. Schapire•Fri Feb 26 2010

This work model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.

3178 0

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Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

Alexandros Karatzoglou, Balázs Hidasi•Sun Jun 11 2017

This work introduces novel ranking loss functions tailored to RNNs in the recommendation setting that allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches.

923 0

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Training Deep AutoEncoders for Collaborative Filtering

Boris Ginsburg, Oleksii Kuchaiev•Fri Aug 04 2017

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 and a new training algorithm based on iterative output re-feeding to overcome natural sparseness of collaborate filtering is proposed.

87 0

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

Fuzheng Zhang, Xing Xie, Wenjie Li, Hongwei Wang, Miao Zhao, M. Guo, Jialin Wang•Thu Mar 08 2018

This paper proposes RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems, and demonstrates that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

1174 0

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Knowledge Graph Convolutional Networks for Recommender Systems

Xing Xie, Wenjie Li, Hongwei Wang, Miao Zhao, M. Guo•Sun Mar 17 2019

This paper proposes Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that captures inter-item relatedness effectively by mining their associated attributes on the KG.

1064 0

Paper Graph

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