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Title of Journal: Multimed Tools Appl

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Abbravation: Multimedia Tools and Applications

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Springer US

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DOI

10.1007/s11373-007-9193-z

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1573-7721

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Implementing situationaware and useradaptive mus

Authors: Seungmin Rho Seheon Song Yunyoung Nam Eenjun Hwang Minkoo Kim
Publish Date: 2011/05/11
Volume: 65, Issue: 2, Pages: 259-282
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Abstract

With the advent of the ubiquitous era many studies have been devoted to various situationaware services in the semantic web environment One of the most challenging studies involves implementing a situationaware personalized music recommendation service which considers the user’s situation and preferences Situationaware music recommendation requires multidisciplinary efforts including lowlevel feature extraction and analysis music mood classification and human emotion prediction In this paper we propose a new scheme for a situationaware/useradaptive music recommendation service in the semantic web environment To do this we first discuss utilizing knowledge for analyzing and retrieving music contents semantically and a user adaptive music recommendation scheme based on semantic web technologies that facilitates the development of domain knowledge and a rule set Based on this discussion we describe our Contextbased Music Recommendation COMUS ontology for modeling the user’s musical preferences and contexts and supporting reasoning about the user’s desired emotions and preferences Basically COMUS defines an upper music ontology that captures concepts on the general properties of music such as titles artists and genres In addition it provides functionality for adding domainspecific ontologies such as music features moods and situations in a hierarchical manner for extensibility Using this context ontology we believe that logical reasoning rules can be inferred based on highlevel implicit knowledge such as situations from lowlevel explicit knowledge As an innovation our ontology can express detailed and complicated relations among music clips moods and situations which enables users to find appropriate music We present some of the experiments we performed as a casestudy for music recommendation

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