MUSIC INFORMATION RETRIEVAL USING SIMILARITY BASED RELEVANCE RANKING TECHNIQUES

Abstract

The purpose of this proposed study activity is to construct a system for the job of automatically assessing the relevance of music datasets, which will be used in future work. Determine item similarity is an important job in a recommender system since it determines if two items are similar. Participants’ systems must provide a list of suggested music that may be added to a given playlist based on a set of playlist characteristics, which will work along with the algorithms designed to provide other similar songs. Specifically, in this study, the challenges of detecting music similarity only on the basis of song information and tags given by users have been addressed. The proposed technique has been tested using a variety of machine learning algorithms to see how well it performs. tf-idf and Word2Vec are the methods used to model the dataset and generate feature vectors. It has also been found we that machine learning techniques, including Collaborative Filtering, KNN, Frequent Pattern Growth, and Matrix Factorization, have a greater influence on relevance ranking than traditional methods.