Recommendation Systems for Choosing Online Learning Resources A Hands-On Approach

Arkajit Saha, Shreya Dey, Monideepa Roy, Sujoy Datta, Pushpendu Kar

Research output: Chapter in Book/Conference proceedingBook Chapterpeer-review


The online learning space and resources have been growing exponentially and have produced a challenge in the form of information overload. To overcome this challenge, recommendation systems that have the ability to recommend actions, learning resources or links for users, according to their interests, have been proposed. Recommender systems can influence and guide users in a personalized way to objects of interest from a large space of available resources. Items similar to those searched, clicked or liked in the past by a given user are recommended by content-based recommender systems. This chapter aims to present the implementation of a course recommendation system using a content-based filtering approach. The web app created uses the features and courses looked up by the users in order to recommend them with courses that they might like. It uses the information provided by the user, and it is able to gather similar ones from the database on the basis of which it curates recommendations.

Original languageEnglish
Title of host publicationRecommender Systems
Subtitle of host publicationA Multi-Disciplinary Approach
PublisherCRC Press
Number of pages24
ISBN (Electronic)9781000886269
ISBN (Print)9781032333212
Publication statusPublished - 1 Jan 2023

ASJC Scopus subject areas

  • Computer Science (all)
  • Mathematics (all)


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