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How people recall, recognize, and reuse search results
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ACM Transactions on Information Systems (TOIS) archive
Volume 26 ,  Issue 4  (September 2008) table of contents
Article No. 19  
Year of Publication: 2008
ISSN:1046-8188
Author
Jaime Teevan  Microsoft Research, Redmond, WA
Publisher
ACM  New York, NY, USA
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ABSTRACT

When a person issues a query, that person has expectations about the search results that will be returned. These expectations can be based on the current information need, but are also influenced by how the searcher believes the search engine works, where relevant results are expected to be ranked, and any previous searches the individual has run on the topic. This paper looks in depth at how the expectations people develop about search result lists during an initial query affect their perceptions of and interactions with future repeat search result lists. Three studies are presented that give insight into how people recall, recognize, and reuse results. The first study (a study of recall) explores what people recall about previously viewed search result lists. The second study (a study of recognition) builds on the first to reveal that people often recognize a result list as one they have seen before even when it is quite different. As long as those aspects that the searcher remembers about the initial list remain the same, other aspects can change significantly. This is advantageous because, as the third study (a study of reuse) shows, when a result list appears to have changed, people have trouble re-using the previously viewed content in the list. They are less likely to find what they are looking for, less happy with the result quality, more likely to find the task hard, and more likely to take a long time searching. Although apparent consistency is important for reuse, people's inability to recognize change makes consistency without stagnation possible. New relevant results can be presented where old results have been forgotten, making both old and new content easy to find.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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REVIEW

"Alan M Arnfeld : Reviewer"

Teevan is a specialist in search and information retrieval for Microsoft Research. In this paper, she addresses three topics that are central to effective search performance by the searcher (user) and effective product design by the product develo  more...