Music Recommendation System with User-Based and Item-Based Collaborative Filtering Technique

Sunitha, M.; Adilakshmi, T.

doi:10.1007/978-981-10-4585-1_22

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Cited by 13 publications

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“…E.g. Celma has shown that the music industry follows a long tail [10]. This algorithm isn't personalized; it simply recommends the most popular items to a user.…”

Section: Popularity Modelmentioning

confidence: 99%

An Automated Music Recommendation System Based on Listener Preferences

Jitendra

Radhika

2021

Recent Trends in Intensive Computing

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Recommender systems play a vital role in e-commerce. It is a big source of a market that brings people from all over the world to a single place. It has become easy to access and reach the market while sitting anywhere. Recommender systems do a major role in the commerce mobility go smoothly easily as it is a software tool that helps in showing or recommending items based on user’s preferences by analyzing their taste. In this paper, we make a recommender system that would be specifically for music applications. Different people listen to different types of music, so we make note of their taste in music and suggest to them the next song based on their previous choice. This is achieved by using a popularity algorithm, classification, and collaborative filtering. Finally, we make a comparison of the built system for its effectiveness with different evaluation metrics.

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“…E.g. Celma has shown that the music industry follows a long tail [10]. This algorithm isn't personalized; it simply recommends the most popular items to a user.…”

Section: Popularity Modelmentioning

confidence: 99%