Foundational Recommender Systems for Business

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Author(s): Prageet Aeron (Management Development Institute, Gurgaon, India)
Copyright: 2023
Pages: 18
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch167

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Given the importance of recommender systems, a detailed understanding of the collaborative filtering systems is imperative. The standard formulation of most collaborative recommender algorithms is either based on prediction of a missing user-item rating or it tends to derive topmost k, users, or items. Collaborative methods could be further divided into user-based filtering and item-based filtering. Regression-based approach can help in combining the advantages of both user-based and content-based methods. Machine learning-based models on the other hand offer further generalization of above models and formally segregate the data modeling, training, and prediction phases. Latent factor models further the same idea by formally incorporating the dimensionality reduction concept and have been found to be very effective. The article is likely to be well received by the academics, especially the doctoral students/researchers in the field of recommender systems as well as the practitioners either utilizing or trying to procure recommendation systems for their organizations.

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