Abstract:
Ranking is a crucial part of information
retrieval. Queries describe the users’ search
intent and therefore they play an essential role in
the context of ranking for information retrieval.
The diverse feature impacts on ranking relevance
with respect to different queries. This paper
tends to consider query difference in learning
ranking function by clustering the queries where
each query cluster represents a group of queries
which have the similar set of important features
for measuring ranking relevance. The success of
clustering usually depends on the representation
of the data. The query features are generated
based on the ranking features values of querydocument pair and Principal Component
Analysis (PCA) is used to construct the
representation of query. To cluster the queries,
bisecting k-means clustering algorithm is used.
RankSVM algorithm is used for model
construction.
