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How Powerful is Graph Convolution for Recommendation?
[article]
2021
arXiv
pre-print
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Graph convolutional networks (GCNs) have recently enabled a popular class of algorithms for collaborative filtering (CF). ...
Based on our framework, we then present a simple and computationally efficient CF baseline, which we shall refer to as Graph Filter based Collaborative Filtering (GF-CF). ...
Next we elaborate our unified framework, which is an extension of (12) with general graph filters. ...
arXiv:2108.07567v1
fatcat:qv54soia5vguro46isbv5ukgbu
Multi-Graph Convolution Collaborative Filtering
[article]
2020
arXiv
pre-print
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In this work, we develop a graph convolution-based recommendation framework, named Multi-Graph Convolution Collaborative Filtering (Multi-GCCF), which explicitly incorporates multiple graphs in the embedding ...
We conduct extensive experiments on four publicly accessible benchmarks, showing significant improvements relative to several state-of-the-art collaborative filtering and graph neural network-based recommendation ...
Neural Graph Collaborative Filtering (NGCF) [11] processes the bipartite user-item interaction graph to learn user and item embeddings.
III. ...
arXiv:2001.00267v1
fatcat:zyej4wufgbdzzlmmzzgjeczzn4
MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
[article]
2022
arXiv
pre-print
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Different from these research, we investigate the GNN-based CF from the perspective of Markov processes for distance learning with a unified framework named Markov Graph Diffusion Collaborative Filtering ...
Collaborative filtering (CF) is widely used by personalized recommendation systems, which aims to predict the preference of users with historical user-item interactions. ...
METHOD Figure 2 and 3 show our unified framework named Markov Graph Diffusion Collaborative Filtering (MGDCF). ...
arXiv:2204.02338v1
fatcat:wlw6zqpvjzcvzggos3yityqnae
Incentivizing Participation in Clinical Trials
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2202.06191v1 fatcat:crwg3kwqazap3gr2ugl7njftny
into contrastive learning for graph collaborative filtering. ...
Formally, the initial feature or learnable embedding of users/items are denoted by 𝑧 (0) in the graph collaborative filtering model [? ]. ...
arXiv:2202.06191v2
fatcat:impmzif57bdbdm5unqdtfmld3a
Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformer
[article]
2021
arXiv
pre-print
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We propagate the information learned fromTCTlayerover the temporal graph to unify sequential patterns and temporal collaborative signals. ...
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging. ...
TimeSVD++ [15] is a representative work which models the temporal information into collaborative filtering (CF) method. It simply treats the bias as a function over time. ...
arXiv:2108.06625v2
fatcat:nyv6p6hhebbqbnnohcy2aqkoau
A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation
[article]
2021
arXiv
pre-print
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Specifically, based on the data usage during recommendation modeling, we divide the work into collaborative filtering and information-rich recommendation: 1) collaborative filtering, which leverages the ...
key source of user-item interaction data; 2) content enriched recommendation, which additionally utilizes the side information associated with users and items, like user profile and item knowledge graph ...
In doing so, we allow a unified framework to summarize neural recommendation models: • Section 2 reviews collaborative filtering models, which forms the basis of personalized recommendation and is the ...
arXiv:2104.13030v3
fatcat:7bzwaxcarrgbhe36teik2rhl6e
Learning Sentiment over Network Embedding for Recommendation System
2021
International Journal of Machine Learning and Computing
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In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding ...
Specifically, we use the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and ...
feature based on knowledge graph with embeddings and integrate with collaborative filtering method for a personalized recommendation. ...
doi:10.18178/ijmlc.2021.11.1.1008
fatcat:zi5lts6l7retlmmva6joqscahm
Light Graph Convolutional Collaborative Filtering with Multi-aspect Information
2021
IEEE Access
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Graph Convolutional Network (GCN) has achieved great success and become a new state-of-the-art for collaborative filtering. ...
In this paper, we propose a Light GCN based Aspect-level Collaborative Filtering model (LGC-ACF) to exploit multi-aspect user-item interaction information. ...
in the embedding propagation process [8] , alleviate the data sparsity problem in collaborative filtering to some extent. ...
doi:10.1109/access.2021.3061915
fatcat:yeus775p5nebvcrpjbzqse7f7q
Federated Knowledge Graphs Embedding
[article]
2021
arXiv
pre-print
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In this paper, we propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous ...
FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. ...
To verify the effectiveness of the proposed FKGE over unified structure of the knowledge graphs, we integrate 11 knowledge graphs into one united knowledge graph by merging aligned entities, and then test ...
arXiv:2105.07615v1
fatcat:il5oopbv65cuzhtyk4ciyqr74u
Recent Advances in Heterogeneous Relation Learning for Recommendation
[article]
2021
arXiv
pre-print
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To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation. ...
We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information. ...
Collaborative Filtering Based Recommendation Collaborative filtering has emerged as the most popular paradigm to build recommender systems. ...
arXiv:2110.03455v1
fatcat:fskj4qdsibfnxefklazdli3tgu
HI2Rec: Exploring Knowledge in Heterogeneous Information for Movie Recommendation
2019
IEEE Access
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Finally, we utilize a collaborative filter approach to generate a precision recommendation list. ...
information from the Linked Open Data and then leverage the knowledge representation learning approach to embed this information as well as real-world datasets' information of recommender systems to a unified ...
RECOMMENDATION APPROACH In this paper, we combine traditional item-based collaborative filtering methods with knowledge graph representation learning methods for recommendation. ...
doi:10.1109/access.2019.2902398
fatcat:67a5wfv3fvbuhb2ybfrhbkrlgi
Rating and aspect-based opinion graph embeddings for explainable recommendations
[article]
2021
arXiv
pre-print
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We then adapt and evaluate state-of-the-art graph embedding techniques over graphs generated from Amazon and Yelp reviews on six domains, outperforming baseline recommenders. ...
The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. ...
Content-based filtering, user-based collaborative filtering [28] , itembased collaborative filtering [28] , matrix factorization using alternated least-squares [15] , and matrix factorization using ...
arXiv:2107.03385v1
fatcat:ujkajmtgavd6ndfys6dm5fisa4
MRP2Rec: Exploring Multiple-step Relation Path Semantics for Knowledge Graph-Based Recommendations
2020
IEEE Access
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[15] proposed a collaborative knowledge base embedding (CKE) model that extends the collaborative filtering (CF) module [16] by introducing knowledge, image and text embedding of items. ...
For integrating large-scale heterogeneous data, collaborative filtering with knowledge graph (CFKG) [19] extends CF by proposing a representation learning approach that embeds heterogeneous entities ...
doi:10.1109/access.2020.3011279
fatcat:tbztgj6qljgsnanmmpubvufcte
Dynamic Graph Collaborative Filtering
[article]
2021
arXiv
pre-print
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Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations of both items and users at the same time. ...
In real-world scenarios, the popularity of items and interests of users change over time. ...
Based on these three update mechanisms mentioned above, we propose Dynamic Graph Collaborative Filtering (DGCF) to employ all of them under a unified framework. ...
arXiv:2101.02844v1
fatcat:y64e6b2tdbbjxgwd2scm4wxs4a
Personalized Course Recommendation System Fusing with Knowledge Graph and Collaborative Filtering
2021
Computational Intelligence and Neuroscience
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In this paper, an algorithm combining knowledge graph and collaborative filtering is proposed. ...
items is calculated, and then, this item semantic information is fused into the collaborative filtering recommendation algorithm. ...
Recommendation Algorithm Fusing with Knowledge Graph and Collaborative Filtering Technology
e Algorithm Model. ...
doi:10.1155/2021/9590502
pmid:34616447
pmcid:PMC8487836
fatcat:6hp6ghyzvzh4xkhs4aissqwcxa
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