Paper Info
Title
Adaptive query suggestion for difficult queries
Abstract
Query suggestion is a useful tool to help users formulate better queries. Although this has been found highly useful globally, its effect on different queries may vary. In this paper, we examine the impact of query suggestion on queries of different degrees of difficulty. It turns out that query suggestion is much more useful for difficult queries than easy queries. In addition, the suggestions for difficult queries should rely less on their similarity to the original query. In this paper, we use a learning-to-rank approach to select query suggestions, based on several types of features including a query performance prediction. As query suggestion has different impacts on different queries, we propose an adaptive suggestion approach that makes suggestions only for difficult queries. We carry out experiments on real data from a search engine. Our results clearly indicate that an approach targeting difficult queries can bring higher gain than a uniform suggestion approach.
Year
DOI
Venue
2012
10.1145/2348283.2348289
SIGIR
Keywords
Field
DocType
query suggestion,easy query,original query,difficult query,different degree,adaptive query suggestion,adaptive suggestion approach,different query,better query,query performance prediction,uniform suggestion approach,learning to rank,search engine
Query optimization,Web search query,Data mining,Query language,Search engine,Information retrieval,Query expansion,Computer science,Sargable,Web query classification,Spatial query
Conference
Citations 
PageRank 
References 
11
0.49
32
Authors
5
Name
Order
Citations
PageRank
Yang Liu1159976.76
Ruihua Song2113859.33
Yu Chen358541.84
Jian-yun Nie43681238.61
Ji-Rong Wen54431265.98