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Neural Collaborative Filtering vs. Matrix Factorization Revisited [article]

Steffen Rendle, Walid Krichene, Li Zhang, John Anderson
2020 arXiv   pre-print
This approach is often referred to as neural collaborative filtering (NCF). In this work, we revisit the experiments of the NCF paper that popularized learned similarities using MLPs.  ...  Embedding based models have been the state of the art in collaborative filtering for over a decade.  ...  This approach is often referred to as neural collaborative filtering (NCF) [16] .  ... 
arXiv:2005.09683v2 fatcat:mkzgijamdndnxo4wnfd5fmo7cq

Neural Collaborative Filtering vs. Matrix Factorization Revisited

Steffen Rendle, Walid Krichene, Li Zhang, John Anderson
2020 Fourteenth ACM Conference on Recommender Systems  
This approach is often referred to as neural collaborative filtering (NCF). In this work, we revisit the experiments of the NCF paper that popularized learned similarities using MLPs.  ...  Embedding based models have been the state of the art in collaborative filtering for over a decade.  ...  This approach is often referred to as neural collaborative filtering (NCF) [17] .  ... 
doi:10.1145/3383313.3412488 dblp:conf/recsys/RendleKZA20 fatcat:mgfmyfinwrbcfjacmznyczevlq

Is Simple Better? Revisiting Non-Linear Matrix Factorization for Learning Incomplete Ratings

Vaibhav Krishna, Tian Guo, Nino Antulov-Fantulin
2018 2018 IEEE International Conference on Data Mining Workshops (ICDMW)  
Matrix factorization techniques have been widely used as a method for collaborative filtering for recommender systems.  ...  The idea that the mapping between the latent user or item factors and the original features is highly nonlinear suggest that classical matrix factorization techniques are no longer sufficient.  ...  In their work, they have shown to have better accuracy than the neural collaborative filtering model by He et. al. [35] .  ... 
doi:10.1109/icdmw.2018.00183 dblp:conf/icdm/KrishnaGA18 fatcat:fpimimjpufa33bpyywkf33moxu

Enhancing VAEs for collaborative filtering

Daeryong Kim, Bongwon Suh
2019 Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19  
Neural network based models for collaborative filtering have started to gain attention recently.  ...  Our work is the first to apply flexible priors to collaborative filtering and show that simple priors (in original VAEs) may be too restrictive to fully model user preferences and setting a more flexible  ...  Research extending the traditional matrix factorization framework to non-linear matrix factorization using neural networks [9] , session-based recommenddation using recurrent neural networks (RNNs) [  ... 
doi:10.1145/3298689.3347015 dblp:conf/recsys/KimS19 fatcat:evkubhaurjhhnlcqr22psgcjcy

LT-OCF: Learnable-Time ODE-based Collaborative Filtering [article]

Jeongwhan Choi, Jinsung Jeon, Noseong Park
2021 arXiv   pre-print
ODE-based Collaborative Filtering (LT-OCF).  ...  Collaborative filtering (CF) is a long-standing problem of recommender systems.  ...  as the target value of the interaction function. (2) Neu-MF is a neural collaborative filtering method [18] .  ... 
arXiv:2108.06208v3 fatcat:6ydpl5mztzdjbokcbjawhijal4

Graph Convolutional Matrix Completion [article]

Rianne van den Berg, Thomas N. Kipf, Max Welling
2017 arXiv   pre-print
Our model shows competitive performance on standard collaborative filtering benchmarks.  ...  We consider matrix completion for recommender systems from the point of view of link prediction on graphs.  ...  Factorization models Many of the most popular collaborative filtering algorithms fall into the class of matrix factorization (MF) models.  ... 
arXiv:1706.02263v2 fatcat:moipp57vhbhxdnwpaagrycznpu

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [article]

Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang
2020 arXiv   pre-print
Recently, in Collaborative Filtering (CF) based Recommender Systems (RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals  ...  In this paper, we revisit GCN based CF models from two aspects.  ...  Linear Residual Graph Convolutional Collaborative Filtering Overall Structure of the Proposed Model In this part, we propose Linear Residual Graph Convolutional Collaborative Filtering (LR-GCCF) which  ... 
arXiv:2001.10167v1 fatcat:qnnjsxenivfpfckt3ujjshv7ca

Revisiting Graph Based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach

Lei Chen, Le Wu, Richang Hong, Kun Zhang, Meng Wang
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Recently, in Collaborative Filtering~(CF) based Recommender Systems~(RS), by treating the user-item interaction behavior as a bipartite graph, some researchers model higher-layer collaborative signals  ...  In this paper, we revisit GCN based CF models from two aspects.  ...  Convolutional Collaborative Filtering (LR-GCCF) which is a general GCN based CF model for recommendation.  ... 
doi:10.1609/aaai.v34i01.5330 fatcat:v2t3jdiaxjdb3inswxywwos7yu

Fast Library Recommendation in Software Dependency Graphs with Symmetric Partially Absorbing Random Walks

Emmanouil Krasanakis, Andreas Symeonidis
2022 Future Internet  
The most accurate project–library recommendation systems employ Graph Neural Networks (GNNs) that learn latent node representations for link prediction.  ...  to guarantee low-pass filtering.  ...  This is effectively a type of collaborative filtering [13] which eventually coalesced to the matrix factorization of the LibSeek tool [14] .  ... 
doi:10.3390/fi14050124 fatcat:fo6nvybqhfe4ncpmfsvvt3thte

Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering [article]

Ga Wu, Maksims Volkovs, Chee Loong Soon, Scott Sanner, Himanshu Rai
2018 arXiv   pre-print
Previous highly scalable one-class collaborative filtering methods such as Projected Linear Recommendation (PLRec) have advocated using fast randomized SVD to embed items into a latent space, followed  ...  Matrix Factorization Matrix factorization models are a subset of latent factorization models, which attempt to uncover latent features of users and items that explain the observations in the implicit feedback  ...  Linear Collaborative Filtering Using optimal user U * and item V * embeddings from equation 8, we can predict unobserved interactions with a simple dot product U * V * T .  ... 
arXiv:1811.00697v1 fatcat:vwqgqkn7lvbo5nh5r6nfasogbq

Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation [article]

Zhichao Xu, Hansi Zeng, Qingyao Ai
2021 arXiv   pre-print
We find that models utilizing only review information can not achieve better performances than vanilla implicit-feedback matrix factorization method.  ...  When utilizing review information as a regularizer or auxiliary information, the performance of implicit-feedback matrix factorization method can be further improved.  ...  Neural [38] Ye Zhang and Byron Wallace. 2015. A sensitivity analysis of (and practition- collaborative filtering vs. matrix factorization revisited.  ... 
arXiv:2106.09665v2 fatcat:6jyktn5tmbchxmgezdhcsajqfm

LFGCN: Levitating over Graphs with Levy Flights [article]

Yuzhou Chen, Yulia R. Gel, Konstantin Avrachenkov
2020 arXiv   pre-print
To highlight comparison of DropEdge vs. P-DropEdge, we show runtime/per epoch of LFGCN + DropEdge vs.  ...  Algorithm 1 P-DropEdge Algorithm Advantages of LFGCN vs.  ... 
arXiv:2009.02365v1 fatcat:6c36zbf3pzaz3aywc3udq557ma

Table of Contents

2020 IEEE Signal Processing Letters  
Shi 810 Negative Binomial Matrix Factorization . J. C.  ...  Ma 1180 Diffusion Collaborative Feedback Particle Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . W. Xia, M. Sun, and Z.  ...  Li 1550 On the Identifiability of Transform Learning for Non-Negative Matrix Factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/lsp.2020.3040840 fatcat:ezrfzwo6tjbkfhohq2tgec4m6y

LFGCN: Levitating over Graphs with Levy Flights

Yuzhou Chen, Yulia R. Gel, Konstantin Avrachenkov
2020 2020 IEEE International Conference on Data Mining (ICDM)  
To highlight comparison of DropEdge vs. P-DropEdge, we show runtime/per epoch of LFGCN + DropEdge vs.  ...  Algorithm 1 P-DropEdge Algorithm Advantages of LFGCN vs.  ... 
doi:10.1109/icdm50108.2020.00109 fatcat:w3a5hjigs5cdjio37bdo6phx7m

Attention on Global-Local Representation Spaces in Recommender Systems [article]

Munlika Rattaphun, Wen-Chieh Fang, Chih-Yi Chiu
2021 arXiv   pre-print
In this study, we present a novel clustering-based collaborative filtering (CF) method for recommender systems.  ...  [5] presented a general framework called neural network-based collaborative filtering (NeuMF) for recommender systems. They used nonlinear neural networks as the user-item interaction function.  ...  The general approaches used in recommender systems include collaborative filtering (CF) and content-based filtering.  ... 
arXiv:2104.12050v2 fatcat:7tubexrdvfbspfas7ocec7niq4
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