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Matrix and Tensor Factorization Techniques for Recommender Systems

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-319-41357-0

ISBN: 978-3-319-41356-3

Publisher: Springer International Publishing - Springer Journals

Jan 31, 2017

Available

Introduction

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 3 –17

[Representing data in lower dimensional spaces has been used extensively in many disciplines such as natural language and image processing, data mining, and information retrieval. Recommender systems deal with challenging issues such as scalability, noise, and sparsity and thus, matrix and tensor...
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Related Work on Matrix Factorization

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 19 –31

[In this chapter, we provide the related work of basic matrix decomposition methods. The first method that we discuss is known as eigenvalue decomposition, which decomposes the initial matrix into a canonical form. The second method is nonnegative matrix factorization (NMF), which factorizes the...
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Performing SVD on Matrices and Its Extensions

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 33 –57

[In this chapter, we describe singular value decomposition (SVD), which is applied on recommender systems. We discuss in detail the method’s mathematical background and present (step by step) the SVD method using a toy example of a recommender system. We also describe in detail UV decomposition....
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Experimental Evaluation on Matrix Decomposition Methods

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 59 –65

[In this chapter, we study the performance of described SVD and UV decomposition algorithms, against an improved version of the original item based CF algorithm combined with SVD. For the UV decomposition method, we will present the appropriate tuning of parameters of its objective function to...
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Related Work on Tensor Factorization

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 69 –80

[In this chapter, we provide a preliminary knowledge overview of tensors. Moreover, we provide the related work on tensor decomposition methods. The first method that is discussed is the Tucker Decomposition (TD) method, which is the underlying tensor factorization model of Higher Order Singular...
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HOSVD on Tensors and Its Extensions

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 81 –93

[This chapter describes in detail tensor decomposition for recommender systems. As our running toy example, we will use a tensor with three dimensions (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...
Available

Experimental Evaluation on Tensor Decomposition Methods

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 95 –99

[In this chapter, we will provide experimental results of tensor decomposition methods on real data sets in social tagging systems (STSs). We will discuss the criteria that we will set for testing all algorithms and the experimental protocol we will follow. Moreover, we will discuss the metrics...
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Conclusions and Future Work

Symeonidis, Panagiotis; Zioupos, Andreas

Pages: 101 –102

[In 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.]

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