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Set Retrieval 2.0

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Daniel Tunkelang
Daniel Tunkelang

This presentation outlines the principles of information seeking as a dialogue and walk though concrete examples that illustrate the principles of human-computer information retrieval (HCIR). The foundation is an interactive set retrieval approach that responds to queries with an overview of the user\'s current context and an organized set of options for incremental exploration. Contextual summaries of document sets optimize the system\'s communication with the user, while query refinement options optimize user\'s communication with the system. By enabling bidirectional communication between the user and the system, we can address the inherent limitations of best-match approaches.

Tecnología
1 de 51
Set Retrieval 2.0 Daniel Tunkelang Chief Scientist, Endeca
howdy! ,[object Object],[object Object],[object Object]
overview ,[object Object],[object Object],[object Object]
let’s quickly review some history…
1947: Hans Peter Luhn
1968: Gerald Salton
1972: Karen Spärck Jones
1980s: lots of progress
1990s – 2000s: WWW
today
so, do we all feel lucky?
recession? what recession?
ask the users…
…though they do have complaints ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
web search vs. enterprise search ,[object Object],[object Object]
enterprise users really have complaints ,[object Object],[object Object],[object Object],[object Object],[object Object]
selection bias?
the library and information science critique ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
the rebuttal "Tell us what to do, and we will do it."
besides, search is 90% solved
we need to call a truce ,[object Object],[object Object],[object Object]
let’s go back to the 80s for a moment
then vs. now ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
requirements
transparency
control
guidance
set retrieval precision = fraction of retrieved documents that are relevant recall = fraction of relevant documents that are retrieved retrieved documents relevant documents
the classic trade-off recall precision
set retrieval: 2 out of 3
set retrieval 2.0 = set retrieval + guidance Did you mean : guidance Related Searches Guidance Counselor Salary Guidance Counselor Job Description Definition of Guidance Guidance Counseling History of Guidance Counseling Child Guidance Career Guidance What Is the Meaning of Guidance Free Marriage Counseling Problems in Marriage Career Exploration Role of School Counselor
guidance vs. mind reading ,[object Object],[object Object],[object Object]
so where does guidance come from?
it’s people!
human-computer information retrieval ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
hcir cheats the trade-off recall precision
but how do we get there?
set retrieval 2.0 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
faceted search guides refinement
showing the right facets: microwaves
showing the right facets: ceiling fans
query-driven clarification before refinement ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
results-driven clarification before refinement Search : storage
taxonomies are so 1990s
dynamic topic facet ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
facets populated using entity extraction apple production
bootstrap on folksonomies
or learn from users
hcir using set retrieval 2.0 ,[object Object],[object Object],[object Object]
think outside the (search) box ,[object Object],[object Object],[object Object],[object Object]
thank you ,[object Object],[object Object],[object Object],[object Object],[object Object]

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Set Retrieval 2.0