A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Co-Clustering Tags and Social Data Sources
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Learning optimal ranking with tensor factorization for tag recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Tensor Decompositions and Applications
SIAM Review
IEEE Transactions on Knowledge and Data Engineering
Multi-way clustering using super-symmetric non-negative tensor factorization
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
In & out zooming on time-aware user/tag clusters
Journal of Intelligent Information Systems
SOHAC: efficient storage of tick data that supports search and analysis
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Storage-optimizing clustering algorithms for high-dimensional tick data
Expert Systems with Applications: An International Journal
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Social tagging is an increasingly popular phenomenon with substantial impact on the way we perceive and understand the Web. For the many Web resources that are not self-descriptive, such as images, tagging is the sole way of associating them with concepts explicitly expressed in text. Consequently, users are encouraged to assign tags to Web resources, and tag recommenders are being developed to stimulate the re-use of existing tags in a consistent way. However, a tag still and inevitably expresses the personal perspective of each user upon the tagged resource. This personal perspective should be taken into account when assessing the similarity of resources with help of tags. In this paper, we focus on similarity-based clustering of tagged items, which can support several applications in social-tagging systems, like information retrieval, providing recommendations, or the establishment of user profiles and the discovery of topics. We show that it is necessary to capture and exploit the multiple values of similarity reflected in the tags assigned to the same item by different users. We model the items, the tags on them and the users who assigned the tags in a multigraph structure. To discover clusters of similar items, we extend spectral clustering, an approach successfully used for the clustering of complex data, into a method that captures multiple values of similarity between any two items. Our experiments with two real social-tagging data sets show that our new method is superior to conventional spectral clustering that ignores the existence of multiple values of similarity among the items.