Song Recommendation System

Song Recommendation System

ABSTRACT

In our project, we will be using a sample data set of songs to find correlations between users and songs so that a new song will be recommended to them based on their previous history. We will implement this project using libraries like NumPy, Pandas. We will also be using Cosine similarity along with Count Vectorizer. Along with this, a front end with flask that will show us the recommended songs when a specific song is processed. Along with the rapid expansion of digital music formats, managing and searching for songs has become significant. Though music information retrieval (MIR) techniques have been made successfully in last ten years, the development of song recommendation systems is still at a very early stage. Therefore, this paper surveys a general framework and state-of-art approaches in recommending song. Two popular algorithms: collaborative filtering (CF) and content-based model (CBM), have been found to perform well. Due to the relatively poor experience in finding songs in long tail and the powerful emotional meanings in songs, two user-centric approaches: context-based model and emotion-based model, have been paid increasing attention. In this paper, three key components in song recommender user modelling, item profiling, and match algorithms are discussed. Six recommendation models and four potential issues towards user experience, are explained. However, subjective song recommendation system has not been fully investigated. To this end, we propose a motivation-based model using the empirical studies of human behaviour, sports education, music psychology. A song recommendation system was developed that can learn users preferences. The system can classify a wide range of stored music using automatic music content analyses. Users can opt for music according to their mood, using such words as ”bright”, ”exciting”, ”quiet”, ”sad” and ”healing”.