Music Implication and Suggestion System Using Collaborative Filtering, Matrix Factorization and KNN Algorithm

EasyChair Preprint 9592

Abstract

The primary objective is to provide recommendation systems that are tailored to each individual listener in terms of music universe, content popularity, familiarity, new releases, appropriation cycle (discovery, repetition, pleasure, saturation), genre diversity, surprise, and the continuation of previous exploration (including outside the music platform). We are planning to design an application that advises songs to the user based on their input and their previous song preferences. The API will be used to collect data from this dataset and call into the application. The primary goal is to develop recommendation systems that are customised to each individual listener in terms of the music universe, the popularity of the content, the listener's level of familiarity with the content, the new releases, the appropriation cycle (discovery, repetition, pleasure, saturation), genre diversity, surprise, and the continuation of previous exploration (including outside the music platform). The primary goal is to develop recommendation systems that are customised to each individual listener in terms of the music universe, the popularity of the content, the listener's level of familiarity with the content, the new releases, the appropriation cycle (discovery, repetition, pleasure, saturation), genre diversity, surprise, and the continuation of previous exploration (including outside the music platform). We intend to build a programme that would provide the user with song recommendations based on the information they provide as well as their past musical tastes. The application programming interface (API) will be utilised to retrieve data from this dataset and make calls into the application.

Keyphrases: Advanced Encryption Standard, Artificial Neural Network, Cascading Style Sheet, Structured Query Language, Support Vector Machine, computer vision, data base, user interface

@booklet{EasyChair:9592,
  author    = {Suryansh Shrivastava and Kartik Srivastava and S. Poornima},
  title     = {Music Implication and Suggestion System Using Collaborative Filtering, Matrix Factorization and KNN Algorithm},
  howpublished = {EasyChair Preprint 9592},
  year      = {EasyChair, 2023}}