Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity

Zehra Cataltepe (Istanbul Technical University, Turkey) and Berna Altinel (Istanbul Technical University, Turkey)
DOI: 10.4018/978-1-60566-306-7.ch010
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Abstract

As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative, content-based and case-based recommendation systems and their hybrids have been used for music recommendation. There are already a number of online music recommendation systems. Although specific user information, such as, demographic data, education, and origin have been shown to affect music preferences, they are usually not collected by the online music recommendation systems, because users would not like to disclose their personal data. Therefore, user models mostly contain information about which music pieces a user liked and which ones s/he did not and when.
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Background

Widespread use of mp3 players and cell-phones and availability of music on these devices according to user demands, increased the need for more accurate Music Information Retrieval (MIR) Systems. Music recommendation is one of the subtasks of MIR Systems and it involves finding music that suits a personal taste (Typke et.al., 2005). The content search in MIR systems could also be used to identify the music played, for example query-by-humming (Ghias et.al., 1995), to identify suspicious sounds recorded by surveillance equipment, to make content-based video retrieval more accurate by means of incorporating music content, to help theaters and film makers find appropriate sound effects (Typke et.al. 2005), to produce audio notification to individuals or groups (Jung & Heckmann 2006).

Music recommendation tasks could be in the form of recommending a single album/song (Logan 2004) or a series of them as in playlist generation (Aucouturier & Pachet, 2002; Alghoniemy & Tewfik, 2000). In addition to containing interesting songs for the user or the user group, a playlist have to obey certain conditions, such as containing all different songs, having a certain duration, having continuity and progression from one song to the next (Aucouturier & Pachet, 2002). Therefore, playlist generation is a harder task than single music item recommendation.

The songs to recommend could contain the audio or MIDI content, as well as, genre, artist, lyrics and other information. The audience of a music recommendation system could be a single person or a group of people (Baccigalupo & Plaza, 2007; McCarthy et.al. 2006). The audience or the songs could be dynamic or mostly static. Depending on these task and user requirements, different algorithms have to be employed for music recommendation. Yahoo Launch!, Last.FM, Pandora (music genome project), CDNow, Audioscrobbler, iRate, MusicStrands, inDiscover (Celma et.al., 2005) are some of the music recommendation projects.

Complete Chapter List

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Table of Contents
Foreword
Bamshad Mobasher
Acknowledgment
Max Chevalier, Christine Julien, Chantal Soule-Dupuy
Chapter 1
Laurent Candillier, Kris Jack, Françoise Fessant, Frank Meyer
The aim of Recommender Systems is to help users to find items that they should appreciate from huge catalogues. In that field, collaborative... Sample PDF
State-of-the-Art Recommender Systems
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Chapter 2
Neal Lathia
Recommender systems generate personalized content for each of its users, by relying on an assumption reflected in the interaction between people... Sample PDF
Computing Recommendations with Collaborative Filtering
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Chapter 3
Edwin Simpson, Mark H. Butler
The increasing amount of available information has created a demand for better, more automated methods of finding and organizing different types of... Sample PDF
Analyzing Communal Tag Relationships for Enhanced Navigation and User Modeling
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Chapter 4
Adaptive User Profiles  (pages 65-87)
Steve Cayzer, Elke Michlmayr
A major opportunity for collaborative knowledge management is the construction of user models which can be exploited to provide relevant... Sample PDF
Adaptive User Profiles
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Chapter 5
Eugene Santos Jr., Hien Nguyen
In this chapter, we study and present our results on the problem of employing a cognitive user model for Information Retrieval (IR) in which a... Sample PDF
Modeling Users for Adaptive Information Retrieval by Capturing User Intent
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Chapter 6
Mihaela Brut, Florence Sedes, Corinne Zayani
Inside the e-learning platforms, it is important to manage the user competencies profile and to recommend to each user the most suitable documents... Sample PDF
Ontology-Based User Competencies Modeling for E-Learning Recommender Systems
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Chapter 7
Colum Foley, Alan F. Smeaton, Gareth J.F. Jones
Traditionally information retrieval (IR) research has focussed on a single user interaction modality, where a user searches to satisfy an... Sample PDF
Combining Relevance Information in a Synchronous Collaborative Information Retrieval Environment
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Chapter 8
Charles Delalonde, Eddie Soulier
This research leverages information retrieval activity in order to build a network of organizational expertise in a distributed R&D laboratory. The... Sample PDF
DemonD: A Social Search Engine Built Upon the Actor-Network Theory
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Chapter 9
Hager Karoui
In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference... Sample PDF
COBRAS: Cooperative CBR Bibliographic Recommender System
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Chapter 10
Zehra Cataltepe, Berna Altinel
As the amount, availability, and use of online music increase, music recommendation becomes an important field of research. Collaborative... Sample PDF
Music Recommendation by Modeling User's Preferred Perspectives of Content, Singer/Genre and Popularity
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Chapter 11
Nima Taghipour, Ahmad Kardan
Information overload is no longer news; the explosive growth of the Internet has made this issue increasingly serious for Web users. Recommender... Sample PDF
Web Content Recommendation Methods Based on Reinforcement Learning
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Chapter 12
Angela Carrillo-Ramos, Manuele Kirsch Pinheiro, Marlène Villanova-Oliver, Jérôme Gensel, Yolande Berbers
The authors of this chapter present a two-fold approach for adapting content information delivered to a group of mobile users. This approach is... Sample PDF
Collaborating Agents for Adaptation to Mobile Users
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Chapter 13
Cristina Gena, Liliana Ardissono
This chapter describes the user-centered design approach we adopted in the development and evaluation of an adaptive Web site. The development of... Sample PDF
A User-Centered Approach to the Retrieval of Information in an Adaptive Web Site
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Chapter 14
Antonella Carbonaro, Rodolfo Ferrini
Active learning is the ability of learners to carry out learning activities in such a way that they will be able to effectively and efficiently... Sample PDF
Personalized Information Retrieval in a Semantic-Based Learning Environment
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Chapter 15
Hanh Huu Hoang, Tho Manh Nguyen, A Min Tjoa
Formulating unambiguous queries in the Semantic Web applications is a challenging task for users. This article presents a new approach in guiding... Sample PDF
A Semantic Web Based Approach for Context-Aware User Query Formulation and Information Retrieval
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