Information Technology Reference
In-Depth Information
With the above feature representations, several learning-to-rank technologies are
applied to optimize the total revenue. One of the models is call RankLogistic. The
model is very similar to RankNet [ 3 ]: its loss function is also defined as a pairwise
cross entropy. The difference lies in that RankLogisitic also involves a regularization
item in its loss function. Another model is called revenue direct optimization. In this
model, the revenue is first formulated as follows:
m (i)
n
r (i)
j
c (i)
j
=
Rev(w)
I
,
(14.2)
min k = j f(w,x (i j )
f(w,x (i k )> 0
{
}
i
=
1
j
=
1
where r (i)
j is the bid price for the j th ad associated with query q i , c (i) j indi-
cates whether the j th ad associated with query q i is clicked by the user, and
I
measures whether the ranking function f can rank the
min k = j f(w,x (i)
j ) f(w,x (i)
{
}
k )> 0
Since the revenue defined above is not continuous, I
is
min k = j f(w,x (i)
f(w,x (i)
{
j )
k )> 0
}
first replaced with k = j I
, and then approximated by changing
the indicator function for a Bernoulli log-likelihood function. In this way, a contin-
uous loss function is obtained as follows:
f(w,x (i j )
f(w,x (i k )> 0
{
}
m (i)
n
log 1
+ e f(w,x (i k ) f(w,x (i j ) .
2
r (i)
j
c (i)
j
L(w) = λ w
+
(14.3)
i
=
1
j
=
1
k
=
j
The above models have been tested on commercial data, with Ad CTR as the
evaluation measure. The experimental results show that the revenue direct optimiza-
tion method can outperform RankLogisic and several other baselines. This empirical
observation is consistent with what we have observed in document retrieval: direct
optimization of evaluation measures can usually outperform pairwise ranking meth-
ods.
14.6 Summary
In addition to the above applications, learning-to-rank technologies have also been
applied in several other applications, such as collaborative filtering [ 11 ], expert find-
ing [ 6 ], and subgroup ranking in database [ 12 ]. From these examples, we can see
that ranking is the key problem in many different applications, and learning-to-rank
technologies can help improve conventional ranking heuristics by learning the opti-
mal ranking model from training data. This demonstrates well the practical impact
of the various research efforts on learning-to-rank. We hope that learning-to-rank
technologies can be applied in more and more applications, and continuously con-
tribute to the development of information retrieval, natural language processing, and
many other fields.
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