Search Engine and Recommendation System for the Music Industry built with JinaAI

TL;DR

This paper presents a music search engine and recommendation system developed with JinaAI, addressing challenges like speed and accuracy by matching user queries with song lyrics for improved search performance.

Contribution

The paper introduces a novel music search and recommendation system utilizing JinaAI to enhance search accuracy and user experience in the music industry.

Findings

Improved search accuracy for song queries.

Enhanced performance with JinaAI framework.

User-friendly music search and recommendation system.

Abstract

One of the most intriguing debates regarding a novel task is the development of search engines and recommendation-based systems in the music industry. Studies have shown a drastic depression in the search engine fields, due to concerning factors such as speed, accuracy and the format of data given for querying. Often people face difficulty in searching for a song solely based on the title, hence a solution is proposed to complete a search analysis through a single query input and is matched with the lyrics of the songs present in the database. Hence it is essential to incorporate cutting-edge technology tools for developing a user-friendly search engine. Jina AI is an MLOps framework for building neural search engines that are utilized, in order for the user to obtain accurate results. Jina AI effectively helps to maintain and enhance the quality of performance for the search engine for the query given. An effective search engine and a recommendation system for the music industry, built with JinaAI.