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Neighborhood-Based Collaborative Recommendations: An Introduction

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Applications of Machine Learning

Part of the book series: Algorithms for Intelligent Systems ((AIS))

Abstract

The amount of information on the World Wide Web (or simply Web) is large and diverse. The explosive growth of information on the Web frequently overwhelms Internet users. Recommender systems (RSs) help individuals who are not able to make decisions from the potentially overwhelming number of alternatives available on the Web. Among various recommendation approaches, collaborative filtering-based recommender systems (CFRS) are the most popular (due to their simplicity and efficiency) and are traditional approaches for recommendations. This chapter describes the main components of a generic framework that may be employed for neighborhood-based collaborative recommendations. Firstly, a few notations have been described to formulate the recommendation process mathematically; hereafter, we have explained the two main categories of neighborhood-based approaches: User-based collaborative filtering (UBCF) and item-based collaborative filtering (IBCF). Secondly, user-item interactions, in the form of rating data (UI-matrix), are analyzed with respect to the recommendation process, i.e., different types of ratings, various ways for collecting rating data, key properties of rating matrices, etc. Finally, the chapter concludes by discussing a few well-known problems associated with the rating data such as sparsity, long-tail, and cold-start problem.

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Authors and Affiliations

  1. Computer Engineering Department, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, 136119, India

    Vijay Verma & Rajesh Kumar Aggarwal

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  1. Vijay Verma
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  2. Rajesh Kumar Aggarwal
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Correspondence to Vijay Verma .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Prashant Johri

  2. Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Haryana, Gurugram (Manesar), Haryana, India

    Jitendra Kumar Verma

  3. Department of Biomedical Engineering, North-Eastern Hill University, Shillong, Meghalaya, India

    Sudip Paul

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Verma, V., Aggarwal, R.K. (2020). Neighborhood-Based Collaborative Recommendations: An Introduction. In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-15-3357-0_7

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  • DOI: https://doi.org/10.1007/978-981-15-3357-0_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3356-3

  • Online ISBN: 978-981-15-3357-0

  • eBook Packages: EngineeringEngineering (R0)

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