Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Finding reliable subgraphs from large probabilistic graphs
Data Mining and Knowledge Discovery
Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems
Compressing tags to find interesting media groups
Proceedings of the 18th ACM conference on Information and knowledge management
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
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In this work we describe a recommendation system based upon user-generated description (tags) of content. In particular, we describe an experimental system (GaMuSo) that consists of more than 140.000 user-defined tags for over 400.000 artists. From this data we constructed a bipartite graph, linking artists via tags to other artists. On the resulting graph we compute related artists for an initial artist of interest. In this work we describe and analyse our system and show that a straightforward recommendation approach leads to related concepts that are overly general, that is, concepts that are related to almost every other concept in the graph. Additionally, we describe a method to provide functional hypothesis for recommendations, given the user insight why concepts are related. GaMuSo is implemented as a webservice and available at: music.biograph.be.