Personalized tag recommendation using graph-based ranking on multi-type interrelated objects

Social tagging is becoming increasingly popular in many Web 2.0 applications where users can annotate resources (e.g. Web pages) with arbitrary keywords (i.e. tags). A tag recommendation module can assist users in tagging process by suggesting relevant tags to them. It can also be directly used to expand the set of tags annotating a resource. The benefits are twofold: improving user experience and enriching the index of resources. However, the former one is not emphasized in previous studies, though a lot of work has reported that different users may describe the same concept in different ways. We address the problem of personalized tag recommendation for text documents. In particular, we model personalized tag recommendation as a “query and ranking” problem and propose a novel graph-based ranking algorithm for interrelated multi-type objects. When a user issues a tagging request, both the document and the user are treated as a part of the query. Tags are then ranked by our graph...

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Ziyu Guan, Jiajun Bu, Qiaozhu Mei, Chun Chen, Can Wang
• Keywords: Graph-based Ranking Algorithm, Information Retrieval, Personalized Tag Recommendation, SIGIR 2009, Tag Recommendation