Design of Ubiquitous Music Recommendation System Using MHMM

The existing music search and recommendation systems obtain results through query or answer and recommend music using data mining techniques. However, it is not possible to provide active services that satisfy customers in smart home environments because these systems consider only static information in web environments. In order to solve these problems, this paper attempts to define context information to use select music and design a ubiquitous music recommendation system that is suited to a user's interests and preferences using Hidden Markov Model for music items. The recommendation system used in this study uses an OSGi framework to recognize context information and increase satisfaction of service.