Music Recommendations Based on Real-Time Data

Examensarbete för kandidatexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/256144
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Type: Examensarbete för kandidatexamen
Bachelor Thesis
Title: Music Recommendations Based on Real-Time Data
Authors: Aurén, Marcus
Bååw, Albin
Hagerman Olzon, David
Karlsson, Tobias
Nilsson, Linnea
Shirmohammad, Pedram
Abstract: This thesis describes the development, implementation and results of a music recommender system that utilizes real time data, namely time and heart rate, for the recommendations. The recommender system was made by combining two systems, the recommender system which predicts a number of song features for a specific user and a ranking system which finds the best matching tracks for these features. Three implementations of the recommender system were implemented for comparison, namely Deep Neural Network, Contextual Bandit and Linear Regression. These implementations were tested with offline evaluation which showed that for our problem, a contextual bandit model had the best accuracy.
Keywords: Data- och informationsvetenskap;Computer and Information Science
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)
Chalmers University of Technology / Department of Computer Science and Engineering (Chalmers)
URI: https://hdl.handle.net/20.500.12380/256144
Collection:Examensarbeten för kandidatexamen // Bachelor Theses



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