lecture18-learning-ranking-handouts-6-per

G introducontoinformaonretrieval

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Unformatted text preview: 
point‐wise learning,
where
we
 try
to
map
items
of
a
certain
relevance
rank
to
 a
subinterval
(e.g,
Crammer
et
al.
2002
PRank)
   But
most
work
does
pair‐wise learning,
where
 the
input
is
a
pair
of
results
for
a
query,
and
 the
class
is
the
relevance
ordering
rela)onship
 between
them
   Train
a
machine
learning
model
to
predict
the
class
r of
a
document‐query
pair

 Perfect Nonrelevant Relevant Weak Relevant Perfect Nonrelevant Introduc)on to Informa)on Retrieval Introduc)on to Informa)on Retrieval Sec.
15.4.2
 The
Ranking
SVM

 Point‐wise
learning
 [Herbrich
et
al.
1999,
2000;
Joachims
et
al.
2002]
   Goal
is
to
learn
a
threshold
to
separate
each
rank
   Aim
is
to
classify
instance
pairs
as
correctly
ranked
or
 incorrectly
ranked
   This
turns
an
ordinal
regression
problem
back
into
a
binary
 classifica)on
problem
   We
want
a
ranking
func)on
f such
that
 ci >
ck iff
f(ψi)
>
f(ψk)   …
or
at
least
one
that
tries
to
do
this
with
minimal
 error
   Suppose
that
f
is
a
linear
func)on

 f(ψi)
=
wψi
 Introduc)on to Informa)on Retrieval Sec.
15.4.2
 Introduc)on to Informa)on Retrieval The
Ranking
SVM

 Adap)ng
the
Ranking
SVM
for...
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This document was uploaded on 02/26/2014.

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