Song Recommendation based on user’s Activity using Ensemble Learning and Clustering
The Song Recommendation System Based on User Schedule project is designed to provide users with personalized music recommendations that match their daily activities and mood swings. With a busy and hectic schedule, it can be challenging to find time to select music that matches a user’s current acti...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05014.pdf |
| _version_ | 1797745586983665664 |
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| author | Joshi Deepali Gade Akshay Savale Phalguni Bhujbal Vinay Goje Pranavraj Mhamane Saniya |
| author_facet | Joshi Deepali Gade Akshay Savale Phalguni Bhujbal Vinay Goje Pranavraj Mhamane Saniya |
| author_sort | Joshi Deepali |
| collection | DOAJ |
| description | The Song Recommendation System Based on User Schedule project is designed to provide users with personalized music recommendations that match their daily activities and mood swings. With a busy and hectic schedule, it can be challenging to find time to select music that matches a user’s current activity and mood. This project aims to provide a solution to this problem by analyzing the user’s daily schedule, including their planned activities and time of day, and using machine learning algorithms to recommend songs that fit their mood and energy level during each activity. The project utilizes a variety of technologies, such as React.js for the front-end and various machine learning algorithms using python for the back-end, to provide a user-friendly interface that allows users to input their schedules and receive song recommendations. |
| first_indexed | 2024-03-12T15:25:27Z |
| format | Article |
| id | doaj.art-1edc570706f0497ea574fa56140a6f19 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-12T15:25:27Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-1edc570706f0497ea574fa56140a6f192023-08-10T13:16:50ZengEDP SciencesITM Web of Conferences2271-20972023-01-01560501410.1051/itmconf/20235605014itmconf_icdsac2023_05014Song Recommendation based on user’s Activity using Ensemble Learning and ClusteringJoshi Deepali0Gade Akshay1Savale Phalguni2Bhujbal Vinay3Goje Pranavraj4Mhamane Saniya5Department of Artificial Intelligence and data science, Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence and data science, Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence and data science, Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence and data science, Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence and data science, Vishwakarma Institute of TechnologyDepartment of Artificial Intelligence and data science, Vishwakarma Institute of TechnologyThe Song Recommendation System Based on User Schedule project is designed to provide users with personalized music recommendations that match their daily activities and mood swings. With a busy and hectic schedule, it can be challenging to find time to select music that matches a user’s current activity and mood. This project aims to provide a solution to this problem by analyzing the user’s daily schedule, including their planned activities and time of day, and using machine learning algorithms to recommend songs that fit their mood and energy level during each activity. The project utilizes a variety of technologies, such as React.js for the front-end and various machine learning algorithms using python for the back-end, to provide a user-friendly interface that allows users to input their schedules and receive song recommendations.https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05014.pdfensemble learningxgboostfirebasek-meansstacking approachclustering |
| spellingShingle | Joshi Deepali Gade Akshay Savale Phalguni Bhujbal Vinay Goje Pranavraj Mhamane Saniya Song Recommendation based on user’s Activity using Ensemble Learning and Clustering ITM Web of Conferences ensemble learning xgboost firebase k-means stacking approach clustering |
| title | Song Recommendation based on user’s Activity using Ensemble Learning and Clustering |
| title_full | Song Recommendation based on user’s Activity using Ensemble Learning and Clustering |
| title_fullStr | Song Recommendation based on user’s Activity using Ensemble Learning and Clustering |
| title_full_unstemmed | Song Recommendation based on user’s Activity using Ensemble Learning and Clustering |
| title_short | Song Recommendation based on user’s Activity using Ensemble Learning and Clustering |
| title_sort | song recommendation based on user s activity using ensemble learning and clustering |
| topic | ensemble learning xgboost firebase k-means stacking approach clustering |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/06/itmconf_icdsac2023_05014.pdf |
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