Context-Based Music Recommendation Algorithm Evaluation

• URL: http://arxiv.org/abs/2112.10612v1
• Date: Thu, 16 Dec 2021 01:46:36 GMT
• Title: Context-Based Music Recommendation Algorithm Evaluation
• Authors: Marissa Baxter, Lisa Ha, Kirill Perfiliev, and Natalie Sayre
• Abstract summary: This paper explores 6 machine learning algorithms and their individual accuracy for predicting whether a user will like a song. The algorithms explored include Logistic Regression, Naive Bayes, Sequential Minimal Optimization (SMO), Multilayer Perceptron (Neural Network), Nearest Neighbor, and Random Forest. With the analysis of the specific characteristics of each song provided by the Spotify API, Random Forest is the most successful algorithm for predicting whether a user will like a song with an accuracy of 84%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial Intelligence (AI ) has been very successful in creating and predicting music playlists for online users based on their data; data received from users experience using the app such as searching the songs they like. There are lots of current technological advancements in AI due to the competition between music platform owners such as Spotify, Pandora, and more. In this paper, 6 machine learning algorithms and their individual accuracy for predicting whether a user will like a song are explored across 3 different platforms including Weka, SKLearn, and Orange. The algorithms explored include Logistic Regression, Naive Bayes, Sequential Minimal Optimization (SMO), Multilayer Perceptron (Neural Network), Nearest Neighbor, and Random Forest. With the analysis of the specific characteristics of each song provided by the Spotify API [1], Random Forest is the most successful algorithm for predicting whether a user will like a song with an accuracy of 84%. This is higher than the accuracy of 82.72% found by Mungekar using the Random Forest technique and slightly different characteristics of a song [2]. The characteristics in Mungekars Random Forest algorithm focus more on the artist and popularity rather than the sonic features of the songs. Removing the popularity aspect and focusing purely on the sonic qualities improve the accuracy of recommendations. Finally, this paper shows how song prediction can be accomplished without any monetary investments, and thus, inspires an idea of what amazing results can be accomplished with full financial research.

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