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How Powerful is Graph Convolution for Recommendation? [article]

Yifei Shen, Yongji Wu, Yao Zhang, Caihua Shan, Jun Zhang, Khaled B. Letaief, Dongsheng Li
2021 arXiv   pre-print
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]

Jianing Sun, Yingxue Zhang, Chen Ma, Mark Coates, Huifeng Guo, Ruiming Tang, Xiuqiang He
2020 arXiv   pre-print
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]

Jun Hu, Shengsheng Qian, Quan Fang, Changsheng Xu
2022 arXiv   pre-print
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]

Yingkai Li, Aleksandrs Slivkins
2022 arXiv   pre-print
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]

Ziwei Fan and Zhiwei Liu and Jiawei Zhang and Yun Xiong and Lei Zheng and Philip S. Yu
2021 arXiv   pre-print
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]

Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang
2021 arXiv   pre-print
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

Phatpicha Yochum, Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004 P.R. China, Liang Chang, Tianlong Gu, Manli Zhu
2021 International Journal of Machine Learning and Computing  
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

Denghua Mei, Niu Huang, Xin Li
2021 IEEE Access  
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]

Hao Peng, Haoran Li, Yangqiu Song, Vincent Zheng, Jianxin Li
2021 arXiv   pre-print
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]

Chao Huang
2021 arXiv   pre-print
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

Ming He, Bo Wang, Xiangkun Du
2019 IEEE Access  
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]

Iván Cantador, Andrés Carvallo, Fernando Diez
2021 arXiv   pre-print
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

Ting Wang, Daqian Shi, Zhaodan Wang, Shuai Xu, Hao Xu
2020 IEEE Access  
[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]

Xiaohan Li, Mengqi Zhang, Shu Wu, Zheng Liu, Liang Wang, Philip S. Yu
2021 arXiv   pre-print
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

Gongwen Xu, Guangyu Jia, Lin Shi, Zhijun Zhang, Ahmed Mostafa Khalil
2021 Computational Intelligence and Neuroscience  
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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