Information Retrieval and Ranking
A learning method is used to generate ranking models. The learning method can create a ranking function that assigns scores to documents and then ranks the documents using the scores. In this learning method, a training set along with performance measures are used to generate weak rankers which a used in the ranking model. During information retrieval, for a given query, the system may return a ranked list of documents in descending order of the relevance scores.
1 . A method comprising:
fetching training data;
fetching one or more parameters that include performance measures as applied to the training data;
creating a weak ranker based on the performance parameters;
assigning a weight to the weak ranker; and
determining whether additional weak rankers are to be created.
2 . The method of claim 1 , wherein the fetching training data includes a set of query elements, a set of retrieved documents corresponding to each of the query elements, and pre-calculated relevance scores for the retrieved documents.
3 . The method of claim 1 , wherein the fetching one or more parameters includes performance parameters represented by a permutation created using a ranking function on a set of documents and user defined relevance scores.
4 . The method of claim 1 , wherein the creating the weak ranker is constructed with the training data having a weight distribution, and goodness of the weak ranker is measured by a performance measured weighted by the weight distribution.
5 . The method of claim 1 , wherein assigning a weak ranker weight is defined by the equation:
α
t
=
1
2
·
ln
∑
i
=
1
m
P
t
(
i
)
{
1
+
E
(
π
(
q
i
,
d
i
,
h
t
)
,
y
i
)
}
∑
i
=
1
m
P
t
(
i
)
{
1
-
E
(
π
(
q
i
,
d
i
,
h
t
)
,
y
i
)
}
6 . The method of claim 1 further updating a ranking model after each iteration of rounds of creating weak rankers.
7 . The method of claim 6 , wherein the updating is performed by linearly combining the weak rankers.
8 . The method of claim 6 , further comprising updating distribution weights of the training data after each iteration.
9 . The method of claim 1 as applied to information retrieval, wherein the information retrieval is directed to one of the following: document retrieval, collaborative filtering, key term, extraction, or expert filtering.
10 . The method of claim 1 as applied to natural language processing, wherein the natural language processing is directed to one of the following: machine translation, paraphrasing, and sentiment analysis.
11 . A method used in a ranking model comprising:
inputting a user query;
retrieving documents relevant to the user query;
generating a ranking model to rank the documents, wherein the ranking model is based on performance measures and the ranking model minimizes loss function and the performance measures; and
arranging the ranked documents in an index.
12 . The method of claim 11 , wherein the inputting is from one or more client computing devices.
13 . The method of claim 11 , wherein the retrieving is from distributed locations in a network.
14 . The method of claim 11 , wherein the generating the ranking model includes an exponential loss function.
15 . The method of claim 11 , wherein the generating the ranking module includes a loss function based on information retrieval performance measures.
16 . A computing device comprising:
a processor;
a memory configured to the processor; and
a ranking module in the memory, implementing a learning method to generate a ranking model, wherein the learning method constructs weak rankers based on weighted training data and combines the weak rankers to generate the ranking model.
17 . The computing device of claim 16 , wherein weighted training data is adapted iteratively using weights in accordance with a pre-defined output.
18 . The computing device of claim 16 , wherein the training data includes arbitrary query elements, retrieved documents corresponding to the query elements, and user defined relevance levels to the retrieved documents.
19 . The computing device of claim 16 , wherein the training data is re-weighted after weak rankers are constructed.
20 . The computing device of claim 16 further comprising a search module in the memory, to receive queries as input.