Towards Developing a Content-Based Recommendation System for Classical Music

Cruz, Ana Felicia; Coronel, Andrei D.

doi:10.1007/978-981-15-1465-4_45

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…The proposed model outperforms existing sequence‐based deep learning models and state‐of‐the‐art methods proposed for recommendation systems in most cases. We also prove our novelty of using MIDI data in music recommendation with other music metadata used in our proposed recommendation algorithms 27 …”

Section: Proposed Problem Formulation and Algorithmmentioning

confidence: 69%

“…Brunner et al 26 use a variational auto-encoder for handling polyphonic music with note duration and velocity information of tracks of multiple instruments. Cruz et al 27 extracted features from MIDI data using jSymbolic library and calculate distance metrics for music items and use that as a piece of content information for the recommendation system.…”

Section: Midi In Music Information Retrievalmentioning

confidence: 99%

“…We also prove our novelty of using MIDI data in music recommendation with other music metadata used in our proposed recommendation algorithms. 27…”

Section: Motivationmentioning

confidence: 99%

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Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system

Yadav

Singh

Pal

2022

Computational Intelligence

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Automatic music recommendation is an open research problem that has seen much work in recent years.A common and successful music recommendation approach is collaborative filtering, which has worked well in this domain. One major drawback of this method is that it suffers from a cold-start problem, and it requires a lot of user-personalized information. It is an ineffective mechanism for recommending new and unpopular songs as well as for new users. In this article, we report a hybrid methodology that uses the song's content information. We use MIDI (Musical Instrument Digital Interface) content data, a compressed version of an audio song that contains digital information about a song and is machine-readable. We describe a model called MSA-SRec (MIDI Based Self Attentive Sequential Music Recommendation), a latent factor-based self-attentive deep learning model that uses a substantial amount of sequential information as content information of the song for recommendation generation. We use MIDI data of a song that is under-explored content information for music recommendation. We show that using MIDI as content data with user and item latent vector produces reasonable recommendations. We also demonstrate that using MIDI over other music metadata performs better

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Section: Proposed Problem Formulation and Algorithmmentioning

confidence: 69%

Section: Midi In Music Information Retrievalmentioning

confidence: 99%

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Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system

Yadav

Singh

Pal

2022

Computational Intelligence

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“…As a result, Cruz and Coronel (2020) describe a method for content-based recommendation that makes use of high-level musical qualities to compare classical music. The preliminary findings show that these features and methods can be used to build a content-based classical music recommender (Cruz & Coronel, 2020).…”

Section: Literature Reviewmentioning

confidence: 97%

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

Faruk¹,

GHOSH²

2022

EPSTEM

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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.

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“…The aim of recommendation systems is suggesting relevant items to users. Three main paradigms of recommendation systems are content-based recommender systems [1], collaborative filtering-based recommender systems [2,3], and hybrid approaches [4]. Collaborative filtering methods are divided into memory-based and modelbased methods.…”

Section: Introductionmentioning

confidence: 99%

Improving Multi-class Co-Clustering-Based Collaborative Recommendation Using Item Tags

Gholami¹,

Forghani²

2020

RIA

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Multi-class Co-Clustering (MCoC)-based recommendation system is a method for recommending favorite items to users. It, firstly, groups items and users in a way that users have common interests and their favored items are put in the same group. Then, to estimate unrated items of each group, for each group, an independent collaborative filtering approach is implemented. MCoC-based recommendation system uses rating vectors to determine similar items and to group them together. We expect that the ratings of two items with the same tag be closer than two items with different tags. Therefore, tags can also be used to determine similar items for co-clustering. The purpose of this study is to enhance the performance of co-clustering to increase the accuracy of MCoC-based recommendation system by using item tags in addition to item rating vectors. Our experiments show that mean absolute error of our proposed method is much less than that of MCoC-based recommendation system.

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Towards Developing a Content-Based Recommendation System for Classical Music

Cited by 8 publications

References 9 publications

Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system

Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system

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

Improving Multi-class Co-Clustering-Based Collaborative Recommendation Using Item Tags

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