A sparse gaussian processes classification framework for fast tag suggestions

Tagged data is rapidly becoming more available on the World Wide Web. Web sites which populate tagging services offer a good way for Internet users to share their knowledge. An interesting problem is how to make tag suggestions when a new resource becomes available. In this paper, we address the issue of efficient tag suggestion. We first propose a multi-class sparse Gaussian process classification framework (SGPS) which is capable of classifying data with very few training instances. We suggest a novel prototype selection algorithm to select the best subset of points for model learning. The framework is then extended to a novel multi-class multi-label classification algorithm (MMSG) that transforms tag suggestion into the problem of multi-label ranking. Experiments on bench-mark data sets and real-world data from Del.icio.us and BibSonomy suggest that our model can greatly improve the performance of tag suggestions when compared to the state-of-the-art. Overall, our model requires li...

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Yang Song, Lu Zhang 0007, C. Lee Giles
• Keywords: CIKM 2008, Efficient Tag Suggestion, Information Management, Prototype Selection, Tag Suggestions