Siteseer: personalized navigation for the Web
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Data mining: concepts and techniques
Data mining: concepts and techniques
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A Music Recommendation System Based on Annotations about Listeners' Preferences and Situations
AXMEDIS '05 Proceedings of the First International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution
A music recommendation system based on music and user grouping
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Automatic playlist generation based on tracking user's listening habits
Multimedia Tools and Applications
Effectiveness of note duration information for music retrieval
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
IEEE Transactions on Audio, Speech, and Language Processing
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
MusicalHeart: a hearty way of listening to music
Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems
Music recommendation using text analysis on song requests to radio stations
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
A music hobbyist listens to different types of music at different times of the day. Thus, an automatic music playlist generator that can adjust to the hobbyist's daily activities on this basis is necessary in order to generate the appropriate music to suit the user's current activity, whether it is working or driving. Although existing research has introduced various music playlist generators, there is yet a system that generates the music playlist based on time. Hence, in this paper, we present a music playlist generation system, which provides an automatic and personalized music playing service based on the time parameter. This system represents the characteristics of music from features extracted out of both the music's symbolic form and wave data. The kernel of this system is based on a modified artificial neural network. The user's music rating history and the associated time stamps in the user's profile constitute the training data of the modified artificial neural networks. A collaborative method has also been proposed to reduce the effect of the cold start problem upon system initialization. A series of experiments have been carried out to demonstrate the performance of this system.