Music Recommendation System

With the rapid development of digital music formats, managing and searching for songs has become significant. On a regular basis, individuals are dependent on a few advices, regardless of whether it is which motion picture to watch on Friday night or are there any fascinating new items accessible on Amazon. A lot of businesses are now using recommender systems for their benefit like Flipkart, Google and Amazon along with Spotify, Wynk Music, Gaana.com, YouTube, etc. This benefits the businesses by attracting customers and the users by getting better services. In this blog, the main focus is on the various components/algorithms that can be used with machine learning and their comparison to find the best suitable algorithm for the music recommendation system.

As users accumulate digital music in their digital devices, the problem arises for them to manage the large number of tracks in them. If a device contains thousands of tracks, it is difficult, painful, and even impractical for a user to pick suitable tracks to listen to without using pre-determined organizations such as albums, playlists or computationally generated recommendations, which is the topic of this paper. A good recommendation system should be able to minimize the user’s effort required to provide feedback and simultaneously maximize the user’s satisfaction by playing the appropriate song at the right time.

In this blog, we have analyzed various algorithms and compared the results with one another to find the effective algorithm that suits our model. The most common approaches people have used for implementing various recommender systems are collaborative filtering and content-based models. These algorithms aim to find similarities between various users’ various songs and artists. Other than these algorithms we have also compared various machine learning algorithms such as decision tree, logistic regression, k-means and random forest

Content-Based Filtering

Content-Based Filtering algorithms are widely used in recommender systems. The main idea behind content-based filtering algorithms in music recommendation is to find similar songs. If a user likes a particular song, then there is a high probability that the user will also like a song similar to the first one

User-Based Collaborative Filtering

Also known as social filtering, collaborative filtering is based on previous interactions between other users and target objects. All historic data about these interactions are fed into the filtering system to generate a prediction.

Clustering and Classification Algorithms

i.     K-means Clustering

The k-means is a generic and easy to implement clustering algorithm with minimal complications and complexities. In clustering algorithms, the data is accepted and taken as input data and then it uses the mathematical precision of calculations and methods to search particular groups which are of similar items. Clustering algorithms such as k-means algorithm generally divide data into different clusters with a huge amount of data at a large amount of scale. It is a good and easy method to arrange and augment.

ii.     Logistic Regression

It is a process of modelling the probability of a discrete outcome given an input variable. The most common models a binary outcome something that takes only two values such as true or false, yes or no, zero or one. Logistic regression despite the name it is a classification model rather than a regression, it is simple and more efficient methods from binary and linear classification problems. What we basically have to do is to create a classifier for each class that will predict the probability of an input set to belong to that particular class instead of all other classes.

iii.     Decision Tree

Decision Tree learning is a supervised machine learning technique for induing the decision tree from training data. It is a predictive model which is a mapping from observation about an item to conclusions about its target value.  In the tree structures, leaves represent classifications (also referred to as labels), non-leaf nodes are features, and branches represent conjunctions of features that lead to the classifications.

iv.     Random Forest:

Random forest is a non-rigid machine learning algorithm. It is a decision-making algorithm which makes prediction on the basis of various attributes function at nodes is computed and then best node is predicted. The random forest is a compatible and commonly used machine learning type of algorithm that gives good results most of the time. It creates a forest and then makes it random. In simple words random forests creates multiple decision trees and merges them together to obtain more accurate and optimum prediction.

Hybrid Approach

Hybrid approaches, which combine content-based and collaborative algorithms are receiving more attention lately. These algorithms have been proposed to overcome the limitations of content-based and collaborative algorithms by combining content and user ratings. However, these models and methods significantly degrade when they are short of corresponding user access data

Upon testing all models with the help of confusion matrix and plotting the graph for real positive rate vs real negative rate, it is found that the random forest algorithm has the highest true positive against true negative rate. Hence, it can be said that random forest algorithm is the most efficient to be used in music recommendation systems. The hybrid approach also had a good learning rate and accuracy.

Model

Percentage

Logistic Regression

73.4

Random Forest

77.9

Decision Tree

71.2

Collaborative Filtering

74.1

 

Authors: Meghana Anthikad, Sakshi Chavan, Harneet Kaur Dehiya

Comments

  1. This article gave me insights about the music recommendation system. This is what I was looking for!! The blog has it all folks, go give it a read 👌✌

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  2. Very informative blog. It clearly mentions the different techniques for music recommendation in simple words

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  3. Very well written and good explanation

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  4. Really understood the essence of recommendation system!

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  5. "If you can't explain it to a 6 year old, then you don't understand it yourself"
    This blog explains everything in a simple language such that even a layman can understand how recommendation systems work, and the different techniques of recommendation. Amazing work!!

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  6. Great initiative! This will be really useful in improving recommendations

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