Learning Emotion Transitions for A Personalized Playlist Recommender
Date Issued
2009
Date
2009
Author(s)
Chi, Chung-Yi
Abstract
The digitization and online distribution of music content in the Internet era have led to an enormous volume of accessible digital music and diversified the ways with which consumers explore music. In particular, an emerging trend in music exploration is to organize and to search songs according to song emotions. However, research on Automatic Playlist Generation (APG) primarily focuses on leveraging traditional metadata and audio similarity for recommendation. Moreover, Mainstream solutions view APG as a static problem.his thesis argues that the APG problem is better modeled as a continuous optimization problem, and proposes an adaptive preference model for personalized APG based on emotions. The main idea is to collect a user''s behavior in music playing (e.g., rating, skipping and replaying) as immediate feedback in learning the user''s preferences for music emotion within a playlist.einforcement learning is adopted to learn the user''s current preferences, which are used to generate personalized playlists. Learning parameters are tuned via simulation of two hypothetical users. Several evaluation metrics are defined to measure the performance of our approach. A two-month user study is conducted to evaluate the APG solutions. The results show that in most of the evaluation metrices the proposed approach presents a superior performance in comparison with the baseline approach.
Subjects
Automatic Playlist Generation
Music Recommender System
Music Emotion Estimation
Reinforcement Learning
Machine Learning
Type
thesis
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