A review on matrix completion for recommender systems

Chen, Zhaoliang; Wang, Shiping

doi:10.1007/s10115-021-01629-6

Cited by 20 publications

(11 citation statements)

References 85 publications

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“…Collaborative filtering is mainly studied in recommender systems and refers to the ability to exploit the relationships between users to recommend items to the active user according to the ratings of his/her neighbors (cf. [17]). In this paper, we consider the more general sense of collaborative filtering defined as the process of searching for information using strategies that involve several agents.…”

Section: Algorithmic Methodsmentioning

confidence: 99%

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Reliable prediction in the Markov stochastic block model

Duchemin

2023

ESAIM: PS

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We introduce the Markov Stochastic Block Model (MSBM): a growth model for community-based networks where node attributes are assigned through a Markovian dynamic. We rely on HMMs' literature to design prediction methods that are robust to local clustering errors. We focus specifically on the link prediction and collaborative filtering problems and we introduce a new model selection procedure to infer the number of hidden clusters in the network. Our approaches for reliable prediction in MSBMs are not algorithm-dependent in the sense that they can be applied using your favourite clustering tool. In this paper, we use a recent SDP method to infer the hidden communities and we provide theoretical guarantees. In particular, we identify the relevant signal-to-noise ratio (SNR) in our framework and we prove that the misclassification error decays exponentially fast with respect to this SNR.

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Section: Algorithmic Methodsmentioning

confidence: 99%

“…For any k, l ∈ [K] and i, j ∈ [n] with i < j, χ(i,j) k,l is the estimate of χ (i,j) k,l computed using Eq. (17).…”

Section: Appendix D Spectral Gaps and Mixing Times For Markov Chainsmentioning

confidence: 99%

Reliable prediction in the Markov stochastic block model

Duchemin

2023

ESAIM: PS

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“…Matrix completion comprises filling the missing entries of a partially observed matrix. While its staple application is in recommender systems [285] it is also used in bioinformatics [286], signal processing [287], and chemistry [288], to name a few. Graphs have been used to capture the structural side information among the rows and columns of this matrix and the entries are treated as signals over these graphs.…”

Section: Matrix Completion and Collaborative Filteringmentioning

confidence: 99%

Graph Filters for Signal Processing and Machine Learning on Graphs

Isufi¹,

Gama²,

Shuman³

et al. 2022

Preprint

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Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters are the crux of many signal processing and machine learning techniques, including convolutional neural networks. Increasingly, modern data also reside on networks and other irregular domains whose structure is better captured by a graph. To process and learn from such data, graph filters account for the structure of the underlying data domain. In this article, we provide a comprehensive overview of graph filters, including the different filtering categories, design strategies for each type, and trade-offs between different types of graph filters. We discuss how to extend graph filters into filter banks and graph neural networks to enhance the representational power; that is, to model a broader variety of signal classes, data patterns, and relationships. We also showcase the fundamental role of graph filters in signal processing and machine learning applications. Our aim is that this article serves the dual purpose of providing a unifying framework for both beginner and experienced researchers, as well as a common understanding that promotes collaborations between signal processing, machine learning, and application domains.

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“…Marques, B. and others said that in the research of transfer path search algorithm, the k-short circuit algorithm is mainly studied [4]. Chen, Z. believes that in physical networks, the shortest path algorithm is the research hotspot of operations research, automation, computer science and other disciplines, and the core content of resource allocation and traffic network analysis [5]. However, Coles, N. A. said that in the practical application of railway passenger transfer path search, sometimes the shortest path is not the optimal choice of passengers due to its transfer times or arrival time.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm

Dan

Yun

Chen

et al. 2022

Wireless Communications and Mobile Computing

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In order to solve the modeling problem of travel as a service (MaaS) collaborative dispatching system of railway passenger transport hub based on neural network algorithm, meet people’s needs, make up for the lack of high traffic travel pressure, and improve people’s living standards, through 30 random questionnaires, it is found that 28 people think that travel convenience improves their quality of life, and 2 people think that owning a car has little effect on travel convenience. Travel has always been people’s basic living needs. In recent years, with the improvement of people’s living standards, there are more and more urban vehicles. At the same time, the increase of vehicles also directly leads to the increasing pressure of urban traffic travel, and the problems of vehicle emission pollution and traffic congestion are becoming more and more obvious. With the national low-carbon environmental protection policies, green transportation has become the theme of the times. With the continuous development of shared transportation mode and intelligent information technology, the new transportation concept of “Mobility as a Service” based on this technical mode will highly integrate the existing transportation modes and travel services. With the support of MaaS system, people’s travel modes will change greatly. Starting with the MaaS cooperative dispatching system of railway passenger transport hub, a multiparameter fuzzy neural network control system dispatching algorithm is proposed to better help the modeling of MaaS cooperative dispatching system of railway passenger transport hub.

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A review on matrix completion for recommender systems

Cited by 20 publications

References 85 publications

Reliable prediction in the Markov stochastic block model

Reliable prediction in the Markov stochastic block model

Graph Filters for Signal Processing and Machine Learning on Graphs

Modeling of Mobility as a Service (MaaS) Collaborative Dispatching System of Railway Passenger Transport Hub Based on Neural Network Algorithm

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