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Neural Collaborative Filtering vs. Matrix Factorization Revisited
[article]
2020
arXiv
pre-print
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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
2020
Fourteenth ACM Conference on Recommender Systems
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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
2018
2018 IEEE International Conference on Data Mining Workshops (ICDMW)
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2017 pre-print arXiv:1710.05613v2 fatcat:pqknabkxora3blhufkgp6nrwd4Other Versions
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
2019
Proceedings of the 13th ACM Conference on Recommender Systems - RecSys '19
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2019 pre-print arXiv:1911.00936v1 fatcat:u6q3ufnvujdmre54l62mt6ojqy
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]
2021
arXiv
pre-print
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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]
2017
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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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
2022
Future Internet
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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]
2018
arXiv
pre-print
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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]
2021
arXiv
pre-print
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2021 pre-print arXiv:2106.09665v1 fatcat:llg7q4jqsnev7f3rocjz5qb57qOther Versions
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]
2020
arXiv
pre-print
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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
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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
2020
2020 IEEE International Conference on Data Mining (ICDM)
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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]
2021
arXiv
pre-print
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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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