標(biāo)題: Titlebook: Matrix and Tensor Factorization Techniques for Recommender Systems; Panagiotis Symeonidis,Andreas Zioupos Book 2016 The Editor(s) (if appl [打印本頁] 作者: Magnanimous 時(shí)間: 2025-3-21 16:31
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書目名稱Matrix and Tensor Factorization Techniques for Recommender Systems讀者反饋學(xué)科排名
作者: PAC 時(shí)間: 2025-3-21 23:43
2191-5768 blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, reco978-3-319-41356-3978-3-319-41357-0Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: STAT 時(shí)間: 2025-3-22 01:07
ies eine vollst?ndige Aufz?hlung sein k?nnte. überhaupt ist die Entstehung der Theorie der Splines ein Beispiel für eine Entwicklung, die durch praktische Erfordernisse ins Leben gerufen wurde. Diese praktischen Erfordernisse bestanden damals in der Notwendigkeit, über anwendbare Methoden zur glatte作者: 抵制 時(shí)間: 2025-3-22 07:36 作者: Evacuate 時(shí)間: 2025-3-22 12:14
Matrix and Tensor Factorization Techniques for Recommender Systems作者: 嚴(yán)厲批評 時(shí)間: 2025-3-22 14:12
Introduction, and information retrieval. Recommender systems deal with challenging issues such as scalability, noise, and sparsity and thus, matrix and tensor factorization techniques appear as an interesting tool to be exploited. That is, we can deal with all aforementioned challenges by applying matrix and te作者: 大酒杯 時(shí)間: 2025-3-22 18:47
Related Work on Matrix Factorizationion, which decomposes the initial matrix into a canonical form. The second method is nonnegative matrix factorization (NMF), which factorizes the initial matrix into two smaller matrices with the constraint that each element of the factorized matrices should be nonnegative. The third method is laten作者: CLEFT 時(shí)間: 2025-3-23 01:04
Performing SVD on Matrices and Its Extensionsal background and present (step by step) the SVD method using a toy example of a recommender system. We also describe in detail UV decomposition. This method is an instance of SVD, as we mathematically prove. We minimize an objective function, which captures the error between the predicted and real 作者: 發(fā)微光 時(shí)間: 2025-3-23 01:49
Experimental Evaluation on Matrix Decomposition Methodsalgorithm combined with SVD. For the UV decomposition method, we will present the appropriate tuning of parameters of its objective function to have an idea of how we can get optimized values of its parameters. We will also answer the question if these values are generally accepted or they should be作者: 感激小女 時(shí)間: 2025-3-23 06:30
Related Work on Tensor Factorizationrst method that is discussed is the Tucker Decomposition (TD) method, which is the underlying tensor factorization model of Higher Order Singular Value Decomposition. TD decomposes a tensor into a set of matrices and one small core tensor. The second one is the PARAFAC method (PARAllel FACtor analys作者: 嘮叨 時(shí)間: 2025-3-23 11:24
HOSVD on Tensors and Its Extensions(i.e., user–item–tag). The main factorization method that will be presented in this chapter is higher order SVD (HOSVD), which is an extended version of the Singular Value Decomposition (SVD) method. In this chapter, we will present a step-by-step implementation of HOSVD in our toy example. Then we 作者: 描繪 時(shí)間: 2025-3-23 16:02
Experimental Evaluation on Tensor Decomposition Methodsuss the criteria that we will set for testing all algorithms and the experimental protocol we will follow. Moreover, we will discuss the metrics that we will use (i.e., Precision, Recall, root-mean-square error, etc.). Our goal is to present the main factors that influence the effectiveness of algor作者: 攝取 時(shí)間: 2025-3-23 21:57
en nach bestimmten Glattheitsforderungen verheftet sind. Die Bezeichnung Spline-Funktionen (Spline Functions) geht auf I. J. Schoenberg [1946]zurück. Die so bezeichneten Funktionen waren jedoch schon früher immer wieder bei verschiedenen Aufgabenstellungen benutzt worden. So kann man etwa bereits da作者: 卜聞 時(shí)間: 2025-3-23 23:44 作者: Gingivitis 時(shí)間: 2025-3-24 03:19 作者: osteopath 時(shí)間: 2025-3-24 08:49
Related Work on Matrix Factorizationmethod is CUR decomposition, which confronts the problem of high density in factorized matrices (a problem that is faced when using the SVD method). This chapter concludes with a description of other state-of-the-art matrix decomposition techniques.作者: Ventilator 時(shí)間: 2025-3-24 12:03
HOSVD on Tensors and Its Extensionsr methods for leveraging the quality of recommendations. Finally, we will study limitations of HOSVD and discuss in detail the problem of non-unique tensor decomposition results and how we can deal with this problem. We also discuss other problems in tensor decomposition, e.g., actualization and scalability.作者: ABHOR 時(shí)間: 2025-3-24 15:51
Introductionnsor decomposition methods (also known as factorization methods). In this chapter, we provide some basic definitions and preliminary concepts on dimensionality reduction methods of matrices and tensors. Gradient descent and alternating least squares methods are also discussed. Finally, we present the book outline and the goals of each chapter.作者: 毀壞 時(shí)間: 2025-3-24 19:55 作者: Triglyceride 時(shí)間: 2025-3-25 00:30 作者: adduction 時(shí)間: 2025-3-25 05:47
Book 2016ts well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathem作者: CRANK 時(shí)間: 2025-3-25 07:52
Related Work on Tensor Factorizationzed is the low-order tensor decomposition (LOTD) method. This method has low functional complexity, is uniquely capable of enhancing statistics, and avoids overfitting compared with traditional tensor decompositions such as TD and PARAFAC.作者: Fresco 時(shí)間: 2025-3-25 12:52 作者: 擺動 時(shí)間: 2025-3-25 19:45 作者: 手術(shù)刀 時(shí)間: 2025-3-25 23:34 作者: 偏見 時(shí)間: 2025-3-26 03:40
https://doi.org/10.1007/978-3-319-41357-0Recommender Systems; Information Retrieval; Factorization Methods; Machine Learning; Matrix Factorizatio作者: 推遲 時(shí)間: 2025-3-26 05:51 作者: Accolade 時(shí)間: 2025-3-26 10:53
Matrix and Tensor Factorization Techniques for Recommender Systems978-3-319-41357-0Series ISSN 2191-5768 Series E-ISSN 2191-5776 作者: 大炮 時(shí)間: 2025-3-26 16:17
Conclusions and Future WorkIn this chapter, we will discuss the main conclusions of the experimental evaluation and the limitations of each algorithm, and will provide the future research directions.作者: 輕打 時(shí)間: 2025-3-26 20:05
Multiple Vector Seeds for Protein Alignmenttion of . [3] to reduce noise hits. We model picking a set of vector seeds as an integer programming problem, and give algorithms to choose such a set of seeds. A good set of vector seeds we have chosen allows four times fewer false positive hits, while preserving essentially identical sensitivity a作者: LIKEN 時(shí)間: 2025-3-26 21:07
Irradiation Orientation from Obliquely Viewed Texturecally canonical cases of isotropic Gaussian random surfaces, under collimated illumination. In this investigation we analyze effects of oblique viewing, extending our theory which applied to normal viewing conditions only [5]. The theory for normal views predicts the structure tensors from either th作者: 等待 時(shí)間: 2025-3-27 01:47 作者: Petechiae 時(shí)間: 2025-3-27 07:42
Leveraging Geographic Research, Web Applications and Surveys in the Construction of Educational Strategies in Ecuador,aper takes a geographic research study that discusses territorial imbalances in access to higher education in Ecuador as an example. It describes a methodology based on developing a web application software (WAS) named “Brechas Educativas”. This software aims to disseminate research results and enco作者: 類人猿 時(shí)間: 2025-3-27 12:04 作者: aggressor 時(shí)間: 2025-3-27 14:42
chen neuen Eintr?gen zu aktuellen Themen wie Agilit?t oder D.Um den hochkomplexen Herausforderungen der globalisierten und im digitalen Wandel begriffenen Industrie 4.0 begegnen zu k?nnen, sind oft schnelle und pragmatische Konzepte zur Strukturierung von Problemen n?tig. An dieser Stelle setzt das 作者: 支柱 時(shí)間: 2025-3-27 19:01 作者: 繼而發(fā)生 時(shí)間: 2025-3-28 00:58 作者: Detain 時(shí)間: 2025-3-28 04:19
Carol Ann Macgregor,Brian Fitzpatrickhase. Here we describe how the mintbody-based methods can be applied to track a specific chromosome, such as the inactive X chromosome (Xi), on which genes are repressed through histone H3 Lys27 trimethylation (H3K27me3). When H3K27me3-specific mintbodies are expressed in cells that harbor Xi, the m作者: d-limonene 時(shí)間: 2025-3-28 09:43
Peter G. Isaact office) to help fulfill a short term customer request and for analytical CRM to extract knowledge about customers, enhancing a company’s understanding of their needs, behavior, and long-term expectations. This is increasingly important to corporations as they seek to establish long-term relationsh作者: 欲望 時(shí)間: 2025-3-28 11:01 作者: 雪白 時(shí)間: 2025-3-28 18:05 作者: 一加就噴出 時(shí)間: 2025-3-28 21:35 作者: Charlatan 時(shí)間: 2025-3-29 02:43
The Importance of Neglect in Policy-Making978-0-230-27707-6Series ISSN 2524-728X Series E-ISSN 2524-7298 作者: 牛馬之尿 時(shí)間: 2025-3-29 07:07
Book 2019n. Mithilfe theoretischer Konzepte nach Pierre Bourdieu sowie einem qualitativen Forschungsprozess gem?? der Grounded Theory generiert die Autorin anhand von Interviewdaten einen ganzheitlichen Blick auf Schule. Hierbei werden Felder und Au?eneinflüsse deutlich, auf die Schulen in ihrem Alltag reagi
