Abstract:
Ranking is the central problem for
information retrieval (IR), and employing
machine learning techniques to learn the ranking
function is viewed as a promising approach to
IR. In information retrieval, the users’ queries
often vary a lot from one to another. However
most of existing approaches for ranking do not
explicitly take into consideration the fact that
queries vary significantly along several
dimensions. In this paper we will take into
account the diversity of query type by clustering
the queries. Instead of deriving a single function,
this system attempt to develop several ranking
functions based on the resulting query clusters in
the sense that different queries of the same
cluster should have similar characteristics in
terms of ranking. So, for each query cluster,
there will be its associated ranking model. To
rank the documents for a new query, the system
first find the most suitable cluster for that query
and produce the scoring results depend on that
cluster. The effectiveness of the system will be
tested on LETOR, publicly available benchmark
data set.
