Slideshows for Tag: last-fm

Slideshows for Tag: last-fm http://www.slideshare.net/ http://www.slideshare.net/images/logo.gif Slideshows for Tag: last-fm http://www.slideshare.net/ Sat, 23 May 2009 21:31:30 GMT SlideShare feed for Slideshows for Tag: last-fm 94 Aplicaciones Educativas 2.0 http://www.slideshare.net/annags/94-aplicaciones-educativas-20
Después de bucear mucho por la red buscando aplicaciones útiles para el trabajo con los alumnos en el aula y tras la experiencia de haberlas probado en actividades didácticas, he decidido agrupar 94 de ellas en una presentación. Estas aplicaciones tienen la ventaja de ser: - Herramientas gratuitas. - Con mejoras continuas. - Permiten la socialización. Se integran entre sí. Posibilitan el desarrollo de competencias digitales de alto nivel para la generación del conocimiento. En la presentación, se van mostrando las aplicaciones de la Web 2.0 más útiles para la Educación, desde las que fomentan competencias digitales más sencillas como la navegación, a las más avanzadas de gestión del conocimiento.]]>
Después de bucear mucho por la red buscando aplicaciones útiles para el trabajo con los alumnos en el aula y tras la experiencia de haberlas probado en actividades didácticas, he decidido agrupar 94 de ellas en una presentación. Estas aplicaciones tienen la ventaja de ser: - Herramientas gratuitas. - Con mejoras continuas. - Permiten la socialización. Se integran entre sí. Posibilitan el desarrollo de competencias digitales de alto nivel para la generación del conocimiento. En la presentación, se van mostrando las aplicaciones de la Web 2.0 más útiles para la Educación, desde las que fomentan competencias digitales más sencillas como la navegación, a las más avanzadas de gestión del conocimiento.]]> Sat, 23 May 2009 21:31:30 GMT http://www.slideshare.net/annags/94-aplicaciones-educativas-20 annags@slideshare.net(annags) 94 Aplicaciones Educativas 2.0 annags Después de bucear mucho por la red buscando aplicaciones útiles para el trabajo con los alumnos en el aula y tras la experiencia de haberlas probado en actividades didácticas, he decidido agrupar 94 de ellas en una presentación. Estas aplicaciones tienen la ventaja de ser: - Herramientas gratuitas. - Con mejoras continuas. - Permiten la socialización. Se integran entre sí. Posibilitan el desarrollo de competencias digitales de alto nivel para la generación del conocimiento. En la presentación, se van mostrando las aplicaciones de la Web 2.0 más útiles para la Educación, desde las que fomentan competencias digitales más sencillas como la navegación, a las más avanzadas de gestión del conocimiento. <img src="http://cdn.slidesharecdn.com/aplicacioneseducativas2-090523163223-phpapp02-thumbnail-2?1243114389" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Después de bucear mucho por la red buscando aplicaciones útiles para el trabajo con los alumnos en el aula y tras la experiencia de haberlas probado en actividades didácticas, he decidido agrupar 94 de ellas en una presentación. Estas aplicaciones tienen la ventaja de ser: - Herramientas gratuitas. - Con mejoras continuas. - Permiten la socialización. Se integran entre sí. Posibilitan el desarrollo de competencias digitales de alto nivel para la generación del conocimiento. En la presentación, se van mostrando las aplicaciones de la Web 2.0 más útiles para la Educación, desde las que fomentan competencias digitales más sencillas como la navegación, a las más avanzadas de gestión del conocimiento. 94 Aplicaciones Educativas 2.0

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Turbo Cash Generator Review http://www.slideshare.net/Turbo_Cash_Generator/turbo-cash-generator-review
Twitter Turbo Cash Generator is a Turnkey Money Making System that runs almost on complete Auto-Pilot and practically dumps Money into your Bank Account.]]>
Twitter Turbo Cash Generator is a Turnkey Money Making System that runs almost on complete Auto-Pilot and practically dumps Money into your Bank Account.]]> Mon, 18 May 2009 23:11:39 GMT http://www.slideshare.net/Turbo_Cash_Generator/turbo-cash-generator-review Turbo_Cash_Generator@slideshare.net(Turbo_Cash_Generator) Turbo Cash Generator Review Turbo_Cash_Generator Twitter Turbo Cash Generator is a Turnkey Money Making System that runs almost on complete Auto-Pilot and practically dumps Money into your Bank Account. <img src="http://cdn.slidesharecdn.com/turbocashgeneratorreview-090518181147-phpapp02-thumbnail-2?1242695030" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Twitter Turbo Cash Generator is a Turnkey Money Making System that runs almost on complete Auto-Pilot and practically dumps Money into your Bank Account. Turbo Cash Generator Review

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Help! My iPod thinks I’m emo. http://www.slideshare.net/plamere/help-my-ipod-thinks-im-emo
Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better. Slides from the SXSW 2009 Panel. Speakers: Paul Lamere from The Echo Nest, and Anthony Volodkin from The Hype Machine.]]>
Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better. Slides from the SXSW 2009 Panel. Speakers: Paul Lamere from The Echo Nest, and Anthony Volodkin from The Hype Machine.]]> Mon, 23 Mar 2009 19:48:37 GMT http://www.slideshare.net/plamere/help-my-ipod-thinks-im-emo plamere@slideshare.net(plamere) Help! My iPod thinks I’m emo. plamere Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better. Slides from the SXSW 2009 Panel. Speakers: Paul Lamere from The Echo Nest, and Anthony Volodkin from The Hype Machine. <img src="http://cdn.slidesharecdn.com/sxsw-ipod-emo-v6-090323144842-phpapp01-thumbnail-2?1237867761" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Music recommendation is broken - automatic music recommenders make mistakes that no human would ever make. In this talk, we will explore why recommenders make such dumb mistakes and we will explore some of the new ideas coming from recommendation and music researchers to help make music recommendations better. Slides from the SXSW 2009 Panel. Speakers: Paul Lamere from The Echo Nest, and Anthony Volodkin from The Hype Machine. Help! My iPod thinks I'm emo.

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Radio Ga Ga http://www.slideshare.net/sam2305/radio-ga-ga-1185369
Internet radio]]>
Internet radio]]> Mon, 23 Mar 2009 17:32:49 GMT http://www.slideshare.net/sam2305/radio-ga-ga-1185369 sam2305@slideshare.net(sam2305) Radio Ga Ga sam2305 Internet radio <img src="http://cdn.slidesharecdn.com/radiogaga-090323123324-phpapp01-thumbnail-2?1237829655" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Internet radio Radio Ga Ga

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Redes Sociales http://www.slideshare.net/obaba/redes-sociales-1185100
Presentación sobre cómo los usuarios utilizan las redes sociales. Es una adaptación de una presentación interna en Nurun que hice hace ya más de un año.]]>
Presentación sobre cómo los usuarios utilizan las redes sociales. Es una adaptación de una presentación interna en Nurun que hice hace ya más de un año.]]> Mon, 23 Mar 2009 16:34:05 GMT http://www.slideshare.net/obaba/redes-sociales-1185100 obaba@slideshare.net(obaba) Redes Sociales obaba Presentación sobre cómo los usuarios utilizan las redes sociales. Es una adaptación de una presentación interna en Nurun que hice hace ya más de un año. <img src="http://cdn.slidesharecdn.com/presentacion-090323113408-phpapp02-thumbnail-2?1237826992" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Presentación sobre cómo los usuarios utilizan las redes sociales. Es una adaptación de una presentación interna en Nurun que hice hace ya más de un año. Redes Sociales

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Erik Frey on Collaborative Filters at SXSW http://www.slideshare.net/AntonKast/erik-frey-on-collaborative-filters-at-sxsw
Erik Frey of Last.fm presenting at "Collaborative Filters: The Evolution of Recommendation Engines" at SXSW Interactive, March 14 2009]]>
Erik Frey of Last.fm presenting at "Collaborative Filters: The Evolution of Recommendation Engines" at SXSW Interactive, March 14 2009]]> Thu, 19 Mar 2009 00:01:23 GMT http://www.slideshare.net/AntonKast/erik-frey-on-collaborative-filters-at-sxsw AntonKast@slideshare.net(AntonKast) Erik Frey on Collaborative Filters at SXSW AntonKast Erik Frey of Last.fm presenting at "Collaborative Filters: The Evolution of Recommendation Engines" at SXSW Interactive, March 14 2009 <img src="http://cdn.slidesharecdn.com/erikfrey-090318190147-phpapp02-thumbnail-2?1237420919" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Erik Frey of Last.fm presenting at "Collaborative Filters: The Evolution of Recommendation Engines" at SXSW Interactive, March 14 2009 Erik Frey on Collaborative Filters at SXSW

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How to build desktop apps that help your web app succeed http://www.slideshare.net/MatthewOgle/how-to-build-desktop-apps-that-help-your-web-app-succeed
The Carsonified gang suggested a slightly different spin on a "Lessons from Last.fm" talk, focused around the desktop. Here are the slides that came out of it, along with an unveiling of the Last.fm Boffin tech demo. Presented at FOWA Dublin, 6 March 2009]]>
The Carsonified gang suggested a slightly different spin on a "Lessons from Last.fm" talk, focused around the desktop. Here are the slides that came out of it, along with an unveiling of the Last.fm Boffin tech demo. Presented at FOWA Dublin, 6 March 2009]]> Mon, 09 Mar 2009 13:31:42 GMT http://www.slideshare.net/MatthewOgle/how-to-build-desktop-apps-that-help-your-web-app-succeed MatthewOgle@slideshare.net(MatthewOgle) How to build desktop apps that help your web app succeed MatthewOgle The Carsonified gang suggested a slightly different spin on a "Lessons from Last.fm" talk, focused around the desktop. Here are the slides that came out of it, along with an unveiling of the Last.fm Boffin tech demo. Presented at FOWA Dublin, 6 March 2009 <img src="http://cdn.slidesharecdn.com/dublin-090309083143-phpapp01-thumbnail-2?1236666842" alt ="" style="border:1px solid #C3E6D8;float:right;" /> The Carsonified gang suggested a slightly different spin on a "Lessons from Last.fm" talk, focused around the desktop. Here are the slides that came out of it, along with an unveiling of the Last.fm Boffin tech demo. Presented at FOWA Dublin, 6 March 2009 How to build desktop apps that help your web app succeed

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Music Recommendation and Discovery in the Long Tail http://www.slideshare.net/ocelma/celma-ph-d-defense-1067735
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail. Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user’s perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user’s relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.]]>
Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail. Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user’s perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user’s relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like.]]> Wed, 25 Feb 2009 11:16:48 GMT http://www.slideshare.net/ocelma/celma-ph-d-defense-1067735 ocelma@slideshare.net(ocelma) Music Recommendation and Discovery in the Long Tail ocelma Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail. Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user’s perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user’s relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like. <img src="http://cdn.slidesharecdn.com/CelmaPhDdefense-090225051652-phpapp01-thumbnail-2?1236434277" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Music consumption is biased towards a few popular artists. For instance, in 2007 only 1% of all digital tracks accounted for 80% of all sales. Similarly, 1,000 albums accounted for 50% of all album sales, and 80% of all albums sold were purchased less than 100 times. There is a need to assist people to filter, discover, personalise and recommend from the huge amount of music content available along the Long Tail. Current music recommendation algorithms try to accurately predict what people demand to listen to. However, quite often these algorithms tend to recommend popular -or well-known to the user- music, decreasing the effectiveness of the recommendations. These approaches focus on improving the accuracy of the recommendations. That is, try to make accurate predictions about what a user could listen to, or buy next, independently of how useful to the user could be the provided recommendations. In this Thesis we stress the importance of the user’s perceived quality of the recommendations. We model the Long Tail curve of artist popularity to predict -potentially- interesting and unknown music, hidden in the tail of the popularity curve. Effective recommendation systems should promote novel and relevant material (non-obvious recommendations), taken primarily from the tail of a popularity distribution. The main contributions of this Thesis are: (i) a novel network-based approach for recommender systems, based on the analysis of the item (or user) similarity graph, and the popularity of the items, (ii) a user-centric evaluation that measures the user’s relevance and novelty of the recommendations, and (iii) two prototype systems that implement the ideas derived from the theoretical work. Our findings have significant implications for recommender systems that assist users to explore the Long Tail, digging for content they might like. Music Recommendation and Discovery in the Long Tail

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Capturer des instants quelque part et les publier partout http://www.slideshare.net/titou/capturer-des-instants-quelque-part-et-les-publier-partout
schéma représentant les flux de diffusion de contenus multimédias géolocalisés sur les réseaux sociaux internet et les connections entre les divers périphériques : Nabaztag, iPhone, micro-ordinateurs, appareil photo numérique]]>
schéma représentant les flux de diffusion de contenus multimédias géolocalisés sur les réseaux sociaux internet et les connections entre les divers périphériques : Nabaztag, iPhone, micro-ordinateurs, appareil photo numérique]]> Sat, 07 Feb 2009 18:28:42 GMT http://www.slideshare.net/titou/capturer-des-instants-quelque-part-et-les-publier-partout titou@slideshare.net(titou) Capturer des instants quelque part et les publier partout titou schéma représentant les flux de diffusion de contenus multimédias géolocalisés sur les réseaux sociaux internet et les connections entre les divers périphériques : Nabaztag, iPhone, micro-ordinateurs, appareil photo numérique <img src="http://cdn.slidesharecdn.com/draftnetworkbillet1-1234031125522641-2-thumbnail-2?1235219967" alt ="" style="border:1px solid #C3E6D8;float:right;" /> schéma représentant les flux de diffusion de contenus multimédias géolocalisés sur les réseaux sociaux internet et les connections entre les divers périphériques : Nabaztag, iPhone, micro-ordinateurs, appareil photo numérique Capturer des instants quelque part et les publier partout

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What Is Social Media? http://www.slideshare.net/ubermore/what-is-social-media-presentation-900805
Presentation including principles for successfull social media implementation, showcasing Barack Obamas election, X-box campaign, O2 ’Orgy of fun’.]]>
Presentation including principles for successfull social media implementation, showcasing Barack Obamas election, X-box campaign, O2 ’Orgy of fun’.]]> Thu, 08 Jan 2009 10:24:27 GMT http://www.slideshare.net/ubermore/what-is-social-media-presentation-900805 ubermore@slideshare.net(ubermore) What Is Social Media? ubermore Presentation including principles for successfull social media implementation, showcasing Barack Obamas election, X-box campaign, O2 ’Orgy of fun’. <img src="http://cdn.slidesharecdn.com/what-is-social-media03-1231416536993342-2-thumbnail-2?1232344976" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Presentation including principles for successfull social media implementation, showcasing Barack Obamas election, X-box campaign, O2 ’Orgy of fun’. What Is Social Media?

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alisonquigley_last.fm http://www.slideshare.net/alisonquigley/alisonquigleylastfm-presentation
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]]> Wed, 15 Oct 2008 17:32:11 GMT http://www.slideshare.net/alisonquigley/alisonquigleylastfm-presentation alisonquigley@slideshare.net(alisonquigley) alisonquigley_last.fm alisonquigley <img src="http://cdn.slidesharecdn.com/lastfmforslideshare-1224099297198124-9-thumbnail-2?1224091933" alt ="" style="border:1px solid #C3E6D8;float:right;" /> alisonquigley_last.fm

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Web 2.0 Synergy: Putting the pieces together http://www.slideshare.net/nengard/web-20-synergy-putting-the-pieces-together-presentation
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]]> Wed, 24 Sep 2008 08:35:38 GMT http://www.slideshare.net/nengard/web-20-synergy-putting-the-pieces-together-presentation nengard@slideshare.net(nengard) Web 2.0 Synergy: Putting the pieces together nengard <img src="http://cdn.slidesharecdn.com/sjrlclifecasting-1222252538802087-8-thumbnail-2?1222245343" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Web 2.0 Synergy: Putting the pieces together

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NRKbeta - Webdagene 2008 http://www.slideshare.net/oyvind/nrkbeta-webdagene-2008-presentation
http://oyvindsolstad.com/2008/09/23/webdagene-2008 My presentation from Webdagene in Lillestrøm, Norway, friday 19. September 2008.]]>
http://oyvindsolstad.com/2008/09/23/webdagene-2008 My presentation from Webdagene in Lillestrøm, Norway, friday 19. September 2008.]]> Tue, 23 Sep 2008 08:36:50 GMT http://www.slideshare.net/oyvind/nrkbeta-webdagene-2008-presentation oyvind@slideshare.net(oyvind) NRKbeta - Webdagene 2008 oyvind http://oyvindsolstad.com/2008/09/23/webdagene-2008 My presentation from Webdagene in Lillestrøm, Norway, friday 19. September 2008. <img src="http://cdn.slidesharecdn.com/webdagene-keynote-for-web-pdf3-1222166145417618-9-thumbnail-2?1222159012" alt ="" style="border:1px solid #C3E6D8;float:right;" /> http://oyvindsolstad.com/2008/09/23/webdagene-2008 My presentation from Webdagene in Lillestrøm, Norway, friday 19. September 2008. NRKbeta - Webdagene 2008

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The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree? http://www.slideshare.net/ocelma/the-quest-for-musical-genres-do-the-experts-and-the-wisdom-of-crowds-agree-presentation
This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, HipHop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Thus, showing the fact that a musical genre could have a multi–faceted definition; using expert based classifications, dynamic associations derived from the community driven annotations, and content–based analysis would improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation.]]>
This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, HipHop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Thus, showing the fact that a musical genre could have a multi–faceted definition; using expert based classifications, dynamic associations derived from the community driven annotations, and content–based analysis would improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation.]]> Thu, 18 Sep 2008 20:57:36 GMT http://www.slideshare.net/ocelma/the-quest-for-musical-genres-do-the-experts-and-the-wisdom-of-crowds-agree-presentation ocelma@slideshare.net(ocelma) The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree? ocelma This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, HipHop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Thus, showing the fact that a musical genre could have a multi–faceted definition; using expert based classifications, dynamic associations derived from the community driven annotations, and content–based analysis would improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation. <img src="http://cdn.slidesharecdn.com/celmasordoismir2008v6-1221778402721613-9-thumbnail-2?1221819155" alt ="" style="border:1px solid #C3E6D8;float:right;" /> This paper presents some findings around musical genres. The main goal is to analyse whether there is any agreement between a group of experts and a community, when defining a set of genres and their relationships. For this purpose, three different experiments are conducted using two datasets: the MP3.com expert taxonomy, and last.fm tags at artist level. The experimental results show a clear agreement for some components of the taxonomy (Blues, HipHop), whilst in other cases (e.g. Rock) there is no correlations. Interestingly enough, the same results are found in the MIREX2007 results for audio genre classification task. Thus, showing the fact that a musical genre could have a multi–faceted definition; using expert based classifications, dynamic associations derived from the community driven annotations, and content–based analysis would improve genre classification, as well as other relevant MIR tasks such as music similarity or music recommendation. The Quest for Musical Genres: Do the Experts and the Wisdom of Crowds Agree?

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Social Tags and Music Information Retrieval (Part I) http://www.slideshare.net/plamere/social-tags-and-music-information-retrieval-part-i-presentation
Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear]]>
Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear]]> Fri, 12 Sep 2008 08:55:25 GMT http://www.slideshare.net/plamere/social-tags-and-music-information-retrieval-part-i-presentation plamere@slideshare.net(plamere) Social Tags and Music Information Retrieval (Part I) plamere Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear <img src="http://cdn.slidesharecdn.com/soctagstutfinalpart1-1221216925574935-8-thumbnail-2?1221209726" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Part 1 of the Social Tags and Music Information Retrieval Tutorial. Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear Social Tags and Music Information Retrieval (Part I)

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Social Tags and Music Information Retrieval (Part II) http://www.slideshare.net/plamere/social-tags-and-music-information-retrieval-part-ii-presentation
Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear]]>
Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear]]> Fri, 12 Sep 2008 08:52:17 GMT http://www.slideshare.net/plamere/social-tags-and-music-information-retrieval-part-ii-presentation plamere@slideshare.net(plamere) Social Tags and Music Information Retrieval (Part II) plamere Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear <img src="http://cdn.slidesharecdn.com/soctagstutfinalpart2-1221216647301013-8-thumbnail-2?1221209539" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear Social Tags and Music Information Retrieval (Part II)

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From hits to niches? ...or how popular artists can bias music recommendation and discovery http://www.slideshare.net/ocelma/from-hits-to-niches-or-how-popular-artists-can-bias-music-recommendation-and-discovery-presentation-567947
This paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists. The results from the experiments reveal that, as expected by its inherent social component, the collaborative filtering approach is prone to popularity bias. This has some consequences on the discovery ratio as well as in the navigation through the Long Tail. On the other hand, in both audio content-based and human expert-based approaches artists are linked independently of their popularity. This allows one to navigate from a mainstream artist to a Long Tail artist in just two or three clicks.]]>
This paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists. The results from the experiments reveal that, as expected by its inherent social component, the collaborative filtering approach is prone to popularity bias. This has some consequences on the discovery ratio as well as in the navigation through the Long Tail. On the other hand, in both audio content-based and human expert-based approaches artists are linked independently of their popularity. This allows one to navigate from a mainstream artist to a Long Tail artist in just two or three clicks.]]> Mon, 25 Aug 2008 02:46:36 GMT http://www.slideshare.net/ocelma/from-hits-to-niches-or-how-popular-artists-can-bias-music-recommendation-and-discovery-presentation-567947 ocelma@slideshare.net(ocelma) From hits to niches? ...or how popular artists can bias music recommendation and discovery ocelma This paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists. The results from the experiments reveal that, as expected by its inherent social component, the collaborative filtering approach is prone to popularity bias. This has some consequences on the discovery ratio as well as in the navigation through the Long Tail. On the other hand, in both audio content-based and human expert-based approaches artists are linked independently of their popularity. This allows one to navigate from a mainstream artist to a Long Tail artist in just two or three clicks. <img src="http://cdn.slidesharecdn.com/celma2ndnetflixacmkdd2008-1219639481516681-8-thumbnail-2?1219713928" alt ="" style="border:1px solid #C3E6D8;float:right;" /> This paper presents some experiments to analyse the popularity effect in music recommendation. Popularity is measured in terms of total playcounts, and the Long Tail model is used in order to rank music artists. Furthermore, metrics derived from complex network analysis are used to detect the influence of the most popular artists in the network of similar artists. The results from the experiments reveal that, as expected by its inherent social component, the collaborative filtering approach is prone to popularity bias. This has some consequences on the discovery ratio as well as in the navigation through the Long Tail. On the other hand, in both audio content-based and human expert-based approaches artists are linked independently of their popularity. This allows one to navigate from a mainstream artist to a Long Tail artist in just two or three clicks. From hits to niches? ...or how popular artists can bias music recommendation and discovery

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From product to service http://www.slideshare.net/guest5b4101/from-product-to-service
Slides from Hannah Donovan\’s workshop at FOWD\’08 in London]]>
Slides from Hannah Donovan\’s workshop at FOWD\’08 in London]]> Mon, 16 Jun 2008 19:26:35 GMT http://www.slideshare.net/guest5b4101/from-product-to-service guest5b4101@slideshare.net(guest5b4101) From product to service guest5b4101 Slides from Hannah Donovan\’s workshop at FOWD\’08 in London <img src="http://cdn.slidesharecdn.com/hannahdonovan-1213651595461028-8-thumbnail-2?1213644407" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Slides from Hannah Donovan\’s workshop at FOWD\’08 in London From product to service

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Lastfm http://www.slideshare.net/gueste059d4/lastfm-349559
schoolwerk khbo ict]]>
schoolwerk khbo ict]]> Sat, 12 Apr 2008 14:19:50 GMT http://www.slideshare.net/gueste059d4/lastfm-349559 gueste059d4@slideshare.net(gueste059d4) Lastfm gueste059d4 schoolwerk khbo ict <img src="http://cdn.slidesharecdn.com/lastfm-1208017173028956-8-thumbnail-2?1208009990" alt ="" style="border:1px solid #C3E6D8;float:right;" /> schoolwerk khbo ict Lastfm

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Web 2.0: Twitter, Last.fm, Gmail http://www.slideshare.net/ranieritrecha/web-20-twitter-lastfm-gmail
Ranieri Trecha Jederson Ramos Wanderson Vasconcelos]]>
Ranieri Trecha Jederson Ramos Wanderson Vasconcelos]]> Tue, 04 Mar 2008 22:38:47 GMT http://www.slideshare.net/ranieritrecha/web-20-twitter-lastfm-gmail ranieritrecha@slideshare.net(ranieritrecha) Web 2.0: Twitter, Last.fm, Gmail ranieritrecha Ranieri Trecha Jederson Ramos Wanderson Vasconcelos <img src="http://cdn.slidesharecdn.com/web-20-twitter-lastfm-gmail-1204677526694535-4-thumbnail-2?1204670327" alt ="" style="border:1px solid #C3E6D8;float:right;" /> Ranieri Trecha Jederson Ramos Wanderson Vasconcelos Web 2.0: Twitter, Last.fm, Gmail

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