This paper studies web object classification problem with the novel exploration of social tags. Automatically classifying web objects into manageable semantic categories has long been a fundamental preprocess for indexing, browsing, searching, and mining these objects. The explosive growth of heterogeneous web objects, especially non-textual objects such as products, pictures, and videos, has made the problem of web classification increasingly challenging. Such objects often suffer from a lack of easy-extractable features with semantic information, interconnections between each other, as well as training examples with category labels. In this paper, we explore the social tagging data to bridge this gap. We cast web object classification problem as an optimization problem on a graph of objects and tags. We then propose an efficient algorithm which not only utilizes social tags as enriched semantic features for the objects, but also infers the categories of unlabeled objects from both homogeneous and heterogeneous labeled objects, through the implicit connection of social tags. Experiment results show that the exploration of social tags effectively boosts web object classification. Our algorithm significantly outperforms the state-of-the-art of general classification methods.
Yin, Zhijun, Rui Li, Qiaozhu Mei, and Jiawei Han. "Exploring social tagging graph for web object classification." In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 957-966. ACM, 2009. [link]