Exploring categorization property of social annotations for information retrieval

  • Authors:

  • Peng Li;Bin Wang;Wei Jin;Jian-Yun Nie;Zhiwei Shi;Ben He

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;North Dakota State University, Fargo, USA;University of Montreal, Montreal, Canada;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Graduate University of Chinese Academy of Sciences, Beijing, China

  • Venue:
  • Proceedings of the 20th ACM international conference on Information and knowledge management
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

User generated social annotations provide extra information for describing document contents. In this paper, we propose an effective method to model the categorization property of social annotations and explore the potential of combining it with classical language models for improving retrieval performance. Specifically, a novel TR-LDA model is presented to take annotations as an additional source for generating document contents apart from the document itself. We provide strategies for representing and weighting the categorization property and develop an efficient inference algorithm, where space saving is taken into account. Experiments are carried out on synthetic datasets, where documents and queries come from the standard evaluation conference TREC and annotations come from the website Delicious.com. Our results demonstrate the effectiveness of the proposed method on the ad-hoc retrieval task, which significantly outperforms state-of-art baselines.