IIR chapter 9
1.relevance feedback (RF) is to involve the user in the
retrieval process so as to improve the final result set.
The basic procedure is:
• The user issues a (short, simple) query.
• The system returns an initial set of retrieval
results.
• The user marks some returned documents as relevant
or non-relevant.
• The system computes a better representation of the
information need based on the user feedback.
• The system displays a revised set of retrieval
results.
2.The Rocchio Algorithm
3.Cases where relevance feedback alone is not
sufficient include:
Misspellings.
Cross-language information retrieval.
Mismatch of searcher’s vocabulary versus collection
vocabulary.
4.Pseudo
relevance feedback, also known as blind
relevance feedback, provides a method for automatic local analysis. It
automates the manual part of relevance feedback, so that the user gets improved
retrieval performance without an extended interaction. The method is to do
normal retrieval to find an initial set of most relevant documents, to then assume that the top k ranked documents are relevant, and
finally to do relevance feedback as before under this assumption.
5.Implicit feedback
while users are often reluctant to provide explicit feedback, it is easy to collect implicit feedback in
large quantities for a high volume system, such as a web search engine
6.three global methods for expanding a query:
(1)by simply
aiding the user in doing so,
(2) by using
a manual thesaurus,
(3)through
building a thesaurus automatically.
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