Essays about: "context-aware music recommendation"

Found 2 essays containing the words context-aware music recommendation.

  1. 1. Generating personalized music playlists based on desired mood and individual listening data

    University essay from Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

    Author : Jennifer Svensson; [2023]
    Keywords : music recommendation; context-based music listening; mood regulation; affect regulation; Spotify Audio Features; music-mood classification; context-aware music recommendation;

    Abstract : Music listening is considered one of the most ubiquitous activities in everyday life, and one of the main reasons why people listen is to affect and regulate their mood. The vast availability and unlimited access of music has made it difficult to find relevant music that fits both the context and the preferences of the music listener. READ MORE

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

    University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

    Author : Oktay Bahceci; [2017]
    Keywords : 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;

    Abstract : 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. READ MORE