MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Theses - Dept. of Electrical Engineering and Computer Sciences
  • Electrical Engineering and Computer Sciences - Master's degree
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Adaptive music recommendation system

Author(s)
Yang, Fan, M. Eng. Massachusetts Institute of Technology
Thumbnail
DownloadFull printable version (3.679Mb)
Other Contributors
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Advisor
Steve Ward.
Terms of use
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
While sources of digital music are getting more abundant and music players are becoming increasingly feature-rich, we still struggle to find new music that we may like. This thesis explores the design and implementation of the MusicPlanner - a music recommendation application that utilizes a goal-oriented framework to recommend and play music. Goal-oriented programming approaches problems by modeling them using Goals, Techniques, and a Planner. The Goals are representations of a user's intent, while the Techniques are the methods that can be used to satisfy the Goals. The Planner connects the Goals and Techniques in a user-defined way to find solutions to user's requests. In the MusicPlanner, the Planner defines the top level Goal of recommending music, which can be satisfied by a set of recommendation Techniques. Each of the recommendation Techniques then declares the sub-Goal of playing music, which can be satisfied by a set of play Techniques. The Planner evaluates each of the Techniques and iterates through the results to choose the best set of Techniques to satisfy the top-level goal of music recommendation. The MusicPlanner allows the user to create personal music stations and for each station, constructs a model of user's music taste based on queries and feedback to the songs played. The extensible design of the architecture and the ease of implementing the MusicPlanner show how goal-oriented framework can simplify the work for programmers. In evaluating the performance of the MusicPlanner, we demonstrate that the Planner in the goal-oriented framework outperforms each individual recommendation Technique.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
 
Cataloged from PDF version of thesis.
 
Includes bibliographical references (p. 61-63).
 
Date issued
2010
URI
http://hdl.handle.net/1721.1/61584
Department
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Electrical Engineering and Computer Sciences - Master's degree
  • Electrical Engineering and Computer Sciences - Master's degree

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Facebook Instagram YouTube RSS

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.