NDLI: Learning to rank query suggestions for adhoc and diversity search

Content Provider	Springer Nature Link
Author	Santos, Rodrygo L. T. Macdonald, Craig Ounis, Iadh
Copyright Year	2012
Abstract	Query suggestions have become pervasive in modern web search, as a mechanism to guide users towards a better representation of their information need. In this article, we propose a ranking approach for producing effective query suggestions. In particular, we devise a structured representation of candidate suggestions mined from a query log that leverages evidence from other queries with a common session or a common click. This enriched representation not only helps overcome data sparsity for long-tail queries, but also leads to multiple ranking criteria, which we integrate as features for learning to rank query suggestions. To validate our approach, we build upon existing efforts for web search evaluation and propose a novel framework for the quantitative assessment of query suggestion effectiveness. Thorough experiments using publicly available data from the TREC Web track show that our approach provides effective suggestions for adhoc and diversity search.
Starting Page	429
Ending Page	451
Page Count	23
File Format	PDF
ISSN	13864564
Journal	Information Retrieval
Volume Number	16
Issue Number	4
e-ISSN	15737659
Language	English
Publisher	Springer Netherlands
Publisher Date	2012-09-21
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Web search Learning to rank Query suggestions Relevance Diversity Information Storage and Retrieval Document Preparation and Text Processing Data Mining and Knowledge Discovery Data Structures, Cryptology and Information Theory Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Library and Information Sciences Information Systems

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