This book investigates several research problems which arise in
modern Web Information Retrieval. First of all we consider the fact
that there are many situations where a flat list of ten search
results are not enough, and that the users might desire to have a
larger number of results grouped on-the-fly in folders of similar
topics. In this book, we describe Snaket, a hierarchical clustering
meta-search engine which personalizes searches according to the
clusters selected on-the-fly by users. Second, we consider those
situations where users might desire to access fresh information
such as news articles. We present a new ranking algorithm suitable
for ranking those fresh type of information. Third, we will discuss
numerical methodologies for accelerating the ranking methodologies
used in Web Search. An important achievement for this book is that
we show how to address the above predominant issues of Web
Information Retrieval by using clustering and ranking
methodologies. We demonstrate that both clustering and ranking have
a mutual reinforcement property that has not yet been studied
intensively.
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