Search-based query suggestion

ABSTRACT

In this paper, we proposed a unified strategy to combine query log and search results for query suggestion. In this way, we leverage both the users' search intentions for popular queries and the power of search engines for unpopular queries. The suggested queries are also ranked according to their relevance and qualities; and each suggestion is described with a rich snippet including a photo and related description.

REFERENCES

• 1: Mehran Sahami , Timothy D. Heilman, A web-based kernel function for measuring the similarity of short text snippets, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135834]
• 2: Zhiyong Zhang , Olfa Nasraoui, Mining search engine query logs for query recommendation, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1136004]
• 3: Qiankun Zhao , Steven C. H. Hoi , Tie-Yan Liu , Sourav S. Bhowmick , Michael R. Lyu , Wei-Ying Ma, Time-dependent semantic similarity measure of queries using historical click-through data, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135858]

INDEX TERMS

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

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Search process

General Terms:
Algorithms, Design, Experimentation

Keywords:
log session mining, query representation, query suggestion