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A Comprehensive Survey on Web Recommendations Systems with Special Focus on Filtering Techniques and Usage of Machine Learning

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Computational Vision and Bio-Inspired Computing ( ICCVBIC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1108))

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Abstract

In present scenario, to improve the consumers buying/purchasing experience, use of technological innovations like Recommender system is very prominent. This small yet powerful utility analyzes the buying pattern of a consumer and suggests which items to buy or use. Recommender systems can be applied to varied fields of consumer’s interest like online shopping, ticket booking and other online contents. Most of the e-commerce sites are attracting huge number of potential customers by providing useful suggestions regarding buying a product or service. This technique is mainly based on the use of Machine Learning, which enables the system to make decisions efficiently. Earlier, these recommendations were mainly relying on filtering process such as collaborative filtering, content based, knowledge based, demographic and hybrid filtering. These filtering techniques, which constitute the recommender system, are discussed in detail in this study. The survey is conducted to describe the various means and methods of recommendation to consumer in real time. This survey presents a comparative study among different types of recommender system based on various parameters and filtering schemes. Moreover, it shows a significant improvement in recommender system by using machine learning based approaches.

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Authors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India

    K. N. Asha & R. Rajkumar

Authors
  1. K. N. Asha
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  2. R. Rajkumar
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Corresponding author

Correspondence to K. N. Asha .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Faculty of Engineering, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Abdullah M. Iliyasu

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Asha, K.N., Rajkumar, R. (2020). A Comprehensive Survey on Web Recommendations Systems with Special Focus on Filtering Techniques and Usage of Machine Learning. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_106

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