�ݺ�ߣshows by User: DanielWolff

�ݺ�ߣshows by User: DanielWolff / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: DanielWolff / Wed, 20 Nov 2013 13:54:06 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: DanielWolff Poster SPOT THE ODD SONG OUT: an online game for collecting music similarity data /slideshow/poster-dmrn7/28463326 posterdmrn7-131120135406-phpapp01
We presented this poster on DMRN+7. Similarity estimation is a key topic in Music Information Retrieval with many applications. In scenarios such as music recommendation, user expectations depend on music similarity. Perceived similarity is specific to the individual user and influenced by a number of factors such as cultural background, age and education. Our goal is to adapt similarity models to similarity data from users, taking into account cultural groups of users sharing common attributes (cultural indicators). At this point, there are few similarity datasets openly available, and none contains information on user background.]]>
We presented this poster on DMRN+7. Similarity estimation is a key topic in Music Information Retrieval with many applications. In scenarios such as music recommendation, user expectations depend on music similarity. Perceived similarity is specific to the individual user and influenced by a number of factors such as cultural background, age and education. Our goal is to adapt similarity models to similarity data from users, taking into account cultural groups of users sharing common attributes (cultural indicators). At this point, there are few similarity datasets openly available, and none contains information on user background.]]> Wed, 20 Nov 2013 13:54:06 GMT /slideshow/poster-dmrn7/28463326 DanielWolff@slideshare.net(DanielWolff) Poster SPOT THE ODD SONG OUT: an online game for collecting music similarity data DanielWolff We presented this poster on DMRN+7. Similarity estimation is a key topic in Music Information Retrieval with many applications. In scenarios such as music recommendation, user expectations depend on music similarity. Perceived similarity is specific to the individual user and influenced by a number of factors such as cultural background, age and education. Our goal is to adapt similarity models to similarity data from users, taking into account cultural groups of users sharing common attributes (cultural indicators). At this point, there are few similarity datasets openly available, and none contains information on user background. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posterdmrn7-131120135406-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> We presented this poster on DMRN+7. Similarity estimation is a key topic in Music Information Retrieval with many applications. In scenarios such as music recommendation, user expectations depend on music similarity. Perceived similarity is specific to the individual user and influenced by a number of factors such as cultural background, age and education. Our goal is to adapt similarity models to similarity data from users, taking into account cultural groups of users sharing common attributes (cultural indicators). At this point, there are few similarity datasets openly available, and none contains information on user background.

Poster SPOT THE ODD SONG OUT: an online game for collecting music similarity data from Daniel Wolff

]]> 337 3 https://cdn.slidesharecdn.com/ss_thumbnails/posterdmrn7-131120135406-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 ADAPTING METRICS FOR MUSIC SIMILARITY USING COMPARATIVE RATINGS /slideshow/poster-ismir11/10578304 posterismir11-111213103025-phpapp02
This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisationand can help to better understand such differences. • 5-fold cross validation with test-sets of ~106 binary rankings, evaluate fulfilled rankings Test Set: MLR: 82% mlrDiag:71% •TagATunegamers have to agreeon the “outlier”clip out of 3 •Data for 533 clip triplets Avg. 14 votes per triplet 1019 clips included ]]>
This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisationand can help to better understand such differences. • 5-fold cross validation with test-sets of ~106 binary rankings, evaluate fulfilled rankings Test Set: MLR: 82% mlrDiag:71% •TagATunegamers have to agreeon the “outlier”clip out of 3 •Data for 533 clip triplets Avg. 14 votes per triplet 1019 clips included ]]> Tue, 13 Dec 2011 10:30:23 GMT /slideshow/poster-ismir11/10578304 DanielWolff@slideshare.net(DanielWolff) ADAPTING METRICS FOR MUSIC SIMILARITY USING COMPARATIVE RATINGS DanielWolff This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisationand can help to better understand such differences. • 5-fold cross validation with test-sets of ~106 binary rankings, evaluate fulfilled rankings Test Set: MLR: 82% mlrDiag:71% •TagATunegamers have to agreeon the “outlier”clip out of 3 •Data for 533 clip triplets Avg. 14 votes per triplet 1019 clips included <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posterismir11-111213103025-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisationand can help to better understand such differences. • 5-fold cross validation with test-sets of ~106 binary rankings, evaluate fulfilled rankings Test Set: MLR: 82% mlrDiag:71% •TagATunegamers have to agreeon the “outlier”clip out of 3 •Data for 533 clip triplets Avg. 14 votes per triplet 1019 clips included

ADAPTING METRICS FOR MUSIC SIMILARITY USING COMPARATIVE RATINGS from Daniel Wolff

]]> 191 3 https://cdn.slidesharecdn.com/ss_thumbnails/posterismir11-111213103025-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 Culture-aware Music Recommendation /slideshow/poster-dmrn5p/10578268 posterdmrn5pa3-111213102737-phpapp01
This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisatio nand can help to better understand such differences. ]]>
This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisatio nand can help to better understand such differences. ]]> Tue, 13 Dec 2011 10:27:34 GMT /slideshow/poster-dmrn5p/10578268 DanielWolff@slideshare.net(DanielWolff) Culture-aware Music Recommendation DanielWolff This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisatio nand can help to better understand such differences. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/posterdmrn5pa3-111213102737-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> This poster presents a machine learning approach for analysing user data that specifies song similarity. Understanding how we relate and compare music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary between different cultures. Adapting a generic model to user ratings is useful for personalisatio nand can help to better understand such differences.

Culture-aware Music Recommendation from Daniel Wolff

]]> 221 2 https://cdn.slidesharecdn.com/ss_thumbnails/posterdmrn5pa3-111213102737-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds document Black http://activitystrea.ms/schema/1.0/post

http://activitystrea.ms/schema/1.0/posted

0 https://cdn.slidesharecdn.com/profile-photo-DanielWolff-48x48.jpg?cb=1522987052 www.juppiemusic.com/category/research https://cdn.slidesharecdn.com/ss_thumbnails/posterdmrn7-131120135406-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/poster-dmrn7/28463326 Poster SPOT THE ODD SO... https://cdn.slidesharecdn.com/ss_thumbnails/posterismir11-111213103025-phpapp02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/poster-ismir11/10578304 ADAPTING METRICS FOR M... https://cdn.slidesharecdn.com/ss_thumbnails/posterdmrn5pa3-111213102737-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/poster-dmrn5p/10578268 Culture-aware Music R...