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research-article

Scalability and sparsity issues in recommender datasets: a survey

Author:

Monika SinghAuthors Info & Claims

Knowledge and Information Systems, Volume 62, Issue 1

Pages 1 - 43

https://doi.org/10.1007/s10115-018-1254-2

Published: 01 January 2020 Publication History

Abstract

Recommender systems have been widely used in various domains including movies, news, music with an aim to provide the most relevant proposals to users from a variety of available options. Recommender systems are designed using techniques from many fields, some of which are: machine learning, information retrieval, data mining, linear algebra and artificial intelligence. Though in-memory nearest-neighbor computation is a typical approach for collaborative filtering due to its high recommendation accuracy; its performance on scalability is still poor given a huge user and item base and availability of only few ratings (i.e., data sparsity) in archetypal merchandising applications. In order to alleviate scalability and sparsity issues in recommender systems, several model-based approaches were proposed in the past. However, if research in recommender system is to achieve its potential, there is a need to understand the prominent techniques used directly to build recommender systems or for preprocessing recommender datasets, along with its strengths and weaknesses. In this work, we present an overview of some of the prominent traditional as well as advanced techniques that can effectively handle data dimensionality and data sparsity. The focus of this survey is to present an overview of the applicability of some advanced techniques, particularly clustering, biclustering, matrix factorization, graph-theoretic, and fuzzy techniques in recommender systems. In addition, it highlights the applicability and recent research works done using each technique.

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Alves R(2024)Regionalization-Based Collaborative Filtering: Harnessing Geographical Information in RecommendersACM Transactions on Spatial Algorithms and Systems10.1145/365664110:2(1-23)Online publication date: 21-May-2024
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Index Terms

Scalability and sparsity issues in recommender datasets: a survey
1. Computing methodologies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image Knowledge and Information Systems

Knowledge and Information Systems Volume 62, Issue 1

Jan 2020

418 pages

ISSN:0219-1377

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© Springer-Verlag London Ltd., part of Springer Nature 2018.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 January 2020

Accepted: 14 July 2018

Revision received: 11 June 2018

Received: 19 September 2017

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Alves R(2024)Regionalization-Based Collaborative Filtering: Harnessing Geographical Information in RecommendersACM Transactions on Spatial Algorithms and Systems10.1145/365664110:2(1-23)Online publication date: 21-May-2024
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Yalcin EBilge A(2024)A novel target item-based similarity function in privacy-preserving collaborative filteringThe Journal of Supercomputing10.1007/s11227-024-06221-780:13(19433-19461)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1007/s11227-024-06221-7
Alatrash RPriyadarshini REzaldeen H(2024)Collaborative filtering integrated fine-grained sentiment for hybrid recommender systemThe Journal of Supercomputing10.1007/s11227-023-05600-w80:4(4760-4807)Online publication date: 1-Mar-2024
https://dl.acm.org/doi/10.1007/s11227-023-05600-w
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https://dl.acm.org/doi/10.1145/3609225
Marigowda CMoldovan ASiddig AMuntean CPathak PStynes P(2023)A Novel Hybrid Machine Learning Framework to Recommend E-Commerce ProductsProceedings of the 2023 5th International Conference on Information Technology and Computer Communications10.1145/3606843.3606853(59-67)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3606843.3606853
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