Taagle: efficient, personalized search in collaborative tagging networks
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Location-sensitive resources recommendation in social tagging systems
Proceedings of the 21st ACM international conference on Information and knowledge management
Automatically generating descriptions for resources by tag modeling
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Network-aware search in social tagging applications: instance optimality versus efficiency
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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In a social tagging system, resources (such as photos, video and web pages) are associated with tags. These tags allow the resources to be effectively searched through tag-based keyword matching using traditional IR techniques. We note that in many such systems, tags of a resource are often assigned by a diverse audience of causal users (taggers). This leads to two issues that gravely affect the effectiveness of resource retrieval: (1) Noise: tags are picked from an uncontrolled vocabulary and are assigned by untrained taggers. The tags are thus noisy features in resource retrieval. (2) A multitude of aspects: different taggers focus on different aspects of a resource. Representing a resource using a flattened bag of tags ignores this important diversity of taggers. To improve the effectiveness of resource retrieval in social tagging systems, we propose CubeLSI--a technique that extends traditional LSI to include taggers as another dimension of feature space of resources. We compare CubeLSI against a number of other tag-based retrieval models and show that CubeLSI significantly outperforms the other models in terms of retrieval accuracy. We also prove two interesting theorems that allow CubeLSI to be very efficiently computed despite the much enlarged feature space it employs.