Sökning: "Musikrekommendation"

Hittade 4 uppsatser innehållade ordet Musikrekommendation.

1. 1. Automatic Music Recommendation for Businesses : Using a two-stage Membership model for track recommendation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Svante Haapanen Rollenhagen; [2021]
   Nyckelord :Deep Learning; Music Recommendation; Recommendation Systems; Music Informatics.; Djupinlärning; Musikrekommendation; Rekommendationssystem; Musikinformatik.;

   Sammanfattning : This thesis proposes a two-stage recommendation system for providing music recommendations based on seed playlists as inputs. The goal is to help businesses find relevant and brand-fit music to play in their venues. LÄS MER

3. 2. JMR - Kontextmedveten musikrekommenderare för Spotify

   Kandidat-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

   Författare :Victor Marante; Simon Månsson; [2018]
   Nyckelord :Context-aware; Spotify; Music recommender;

   Sammanfattning : Strömmande musiktjänster erbjuder ett stort utbud av musik. Förutom att användare kan skapa egna listor från detta utbud, erbjuder tjänsterna ofta personliga rekommendationer. Även om rekommendationerna passar användaren väl, passar de inte alltid för situationen de befinner sig i. LÄS MER

4. 3. Deep Neural Networks for Context Aware Personalized Music Recommendation : A Vector of Curation

   Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :Oktay Bahceci; [2017]
   Nyckelord :Information Filtering; Information Retrieval; Search Engine; Search Engines; Recommendation; Music Recommendation; Personalized Recommendation; Personalised Recommendation; Context Aware Recommendation; Recommender Systems; Statistical Learning; Artificial Intelligence; Machine Learning; Deep Learning; Neural Networks; Artificial Neural Networks; Feed Forward Neural Networks; Convolutional Neural Networks; Recurrent Neural Networks; Deep Neural Networks; Embedding;

   Sammanfattning : Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. LÄS MER

5. 4. Music discovery methods using perceptual features

   Master-uppsats, KTH/Skolan för datavetenskap och kommunikation (CSC)

   Författare :Richard Nysäter; [2017]
   Nyckelord :perceptual features; music information retrieval; k-pop; music discovery; music recommendation; MIR; music browser; music website; perceptuella särdrag; musikrekommendation; k-pop; musikhemsida;

   Sammanfattning : Perceptual features are qualitative features used to describe music properties in relation to human perception instead of typical musical theory concepts such as pitches and chords. This report describes a music discovery platform which uses three different methods of music playlist generation to investigate if and how perceptual features work when used for music discovery. LÄS MER