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2021 | OriginalPaper | Chapter

Recommendation Systems Based on Collaborative Filtering Using Autoencoders: Issues and Opportunities

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Authors: Ria Banerjee, Preeti Kathiria, Deepika Shukla

Publisher: Springer Singapore

Published in: Recent Innovations in Computing

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Abstract

With the advancement of deep-learning-based approaches, complex problems under Artificial Intelligence can now be addressed in a comparatively easier way. One such application domain is Recommendation Systems. Recommendation systems are powerful tools in this age of data explosion for providing meaningful insights from data. Collaborative Filtering is one of the popular approaches for building recommendation systems and extensive literary works suggest that it is very effective. In recent years deep-learning-based models have been bounteously applied for the development of recommendation systems using collaborative filtering. Autoencoders are a deep-learning based neural architecture which can be used for implementing collaborative filtering. This paper presents a survey of different autoencoder based models which employ collaborative filtering methodology for making recommendation systems. The paper initially provides an understanding of models and thereafter summarizes various works reported in the literature in the light of the methodology used, taxonomy, datasets used for experimentation, limitations and results reported.

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Title

Recommendation Systems Based on Collaborative Filtering Using Autoencoders: Issues and Opportunities

Book

Recent Innovations in Computing

Print ISBN: 978-981-15-8296-7

Electronic ISBN: 978-981-15-8297-4

https://doi.org/10.1007/978-981-15-8297-4

DOI

https://doi.org/10.1007/978-981-15-8297-4_32

Authors:

Ria Banerjee
Preeti Kathiria
Deepika Shukla

Publisher

Springer Singapore

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