Addressing Longtail Problem using Adaptive Clustering for Music Recommendation System

Sunitha, M.; Adilakshmi, T.; Teja, G. Ravi; Noel, Ayush

doi:10.1007/978-981-16-9669-5_30

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The metric called Tail-P has been proposed and contrasted with user-based and item-based CF models for the adaptive clustering approach. Results show that adaptive clustering outperformed CF models in terms of identifying longtail songs (Sunitha et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

Faruk¹,

GHOSH²

2022

EPSTEM

View full text Add to dashboard Cite

In the modern scenario, everyone uses the internet to find music, movies, products, services and other commodities on a regular basis to make their lives easier. Because of a lot of data on millions of music, movie, products and services on websites, we need a recommender system very much to assist people in making decisions more quickly and easily. In this research study, we have developed an intelligent music recommendation system by integrating a Music Genre Classification (MGC) with different types of Deep Learning Techniques such as RNN-LSTM, GRU and CNN. We have used the GTZAN Genre dataset to training our system. We have extracted the features from GTZAN dataset by the help of Mel Frequency Cepstral Coefficients (MFCCs) then pass the MFCCs into our deep learning networks. After classifying the appropriate music genre, recommended the music from particular genre from the labelled database which has been classified by our system. From our proposed models the GRU, CNN and RNN-LSTM produced about 71%, 72% and 74% respectively in our testing accuracy. The RNN-LSTM has achieved the best accuracy result (74%) among all of our proposed models.

show abstract

Section: Literature Reviewmentioning

confidence: 99%