Category Recommendation in User Specified Structure

Abstract

Tagging has become a main tool for Internet users to describe and advertise various web resources. The relatively flat structure of the tag space poses lots of challenges in tag based query engines. Many data-centric algorithms have been proposed to discover structures from the flat tag space and to improve query results. At the same time, lots of social networking sites start to provide mechanisms allowing users to specify simple hierarchical structures. The group concept in Flickr is a good example of such user specified structure. Users can create a group with predefined themes. Other users can add their resources to several related groups voluntarily or by invitation. These groups are analogue to categories in a cataloguing system. This user specified structure would ideally improve the precision of tag based query. However, categories can be created by any user and public categories like groups are open for users to add resources in. More often than not, a simple category title does not give enough information on its content. In this paper we propose two algorithms, traditional IR cosine similarity approach and frequent pattern matching approach, to recommend categories to a given resource. We evaluate our algorithms using groups and photos from Flickr. Both algorithms achieve promising results in terms of precision in general. We also analyse strength and weakness of the two algorithms with respect to features of test data. We believe such recommendation mechanism is an important complement to any user specified hierarchical structure.