Music Recommendation Bot

This project introduces a Song Recommendation Bot designed to suggest music tracks based on the user's daily experiences and emotional states. By leveraging natural language processing (NLP) techniques, the bot interprets user input describing their day, identifying key emotions and themes. It then maps these insights to a curated music database to provide personalized song recommendations that resonate with the user's current mood and experiences. The bot integrates with popular music streaming services, enabling seamless access to a wide array of tracks. The aim of this project is to enhance the user's music discovery journey by aligning song suggestions with their emotional context, thus creating a more meaningful and engaging listening experience. This documentation outlines the system's architecture, implementation details, key features, and evaluation metrics, demonstrating the bot's capability to understand user sentiments and deliver appropriate music recommendations

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-15
DOI
https://doi.org/10.5281/zenodo.17356633
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Music Recommendation Bot

Zenodo (CERN European Organization for Nuclear Research)
Music and Audio Processing
article

Music Recommendation Bot

article en

Abstract

This project introduces a Song Recommendation Bot designed to suggest music tracks based on the user's daily experiences and emotional states. By leveraging natural language processing (NLP) techniques, the bot interprets user input describing their day, identifying key emotions and themes. It then maps these insights to a curated music database to provide personalized song recommendations that resonate with the user's current mood and experiences. The bot integrates with popular music streaming services, enabling seamless access to a wide array of tracks. The aim of this project is to enhance the user's music discovery journey by aligning song suggestions with their emotional context, thus creating a more meaningful and engaging listening experience. This documentation outlines the system's architecture, implementation details, key features, and evaluation metrics, demonstrating the bot's capability to understand user sentiments and deliver appropriate music recommendations

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 42%
Music and Audio Processing
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.