Identifying vertical search intention of query through social tagging propagation

ABSTRACT

A pressing task during the unification process is to identify a user's vertical search intention based on the user's query. In this paper, we propose a novel method to propagate social annotation, which includes user-supplied tag data, to both queries and VSEs for semantically bridging them. Our proposed algorithm consists of three key steps: query annotation, vertical annotation and query intention identification. Our algorithm, referred to as TagQV, verifies that the social tagging can be propagated to represent Web objects such as queries and VSEs besides Web pages. Experiments on real Web search queries demonstrate the effectiveness of TagQV in query intention identification.

REFERENCES

• 1: Weiyi Meng , Clement Yu , King-Lup Liu, Building efficient and effective metasearch engines, ACM Computing Surveys (CSUR), v.34 n.1, p.48-89, March 2002 [doi>10.1145/505282.505284]
• 2: Dou Shen , Rong Pan , Jian-Tao Sun , Jeffrey Junfeng Pan , Kangheng Wu , Jie Yin , Qiang Yang, Query enrichment for web-query classification, ACM Transactions on Information Systems (TOIS), v.24 n.3, p.320-352, July 2006 [doi>10.1145/1165774.1165776]
• 3: Yiming Yang, An Evaluation of Statistical Approaches to Text Categorization, Information Retrieval, v.1 n.1-2, p.69-90, 1999 [doi>10.1023/A:1009982220290]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Measurement, Performance

Keywords:
metadata, social annotation, vertical search engine (vse).