In this paper we describe Inquirus 2, a metasearch engine
with a dynamic interface that makes individual recommendations
based on user preferences. Users of Inquirus 2 specify
both a keyword query and an information need category.
The combination of the query and information need category
is used to produce a personalized ordering of results
found via a search strategy specific to the user’s need. Since
the search process does not require state about the user 1, no
training is necessary. The dynamic interface allows the display
and ordering of results, as they are found, reducing the
wait time for users.
At NEC Research Institute, researchers have many different
search needs, ranging from searching for organizations 数据挖掘论坛
related to some research area, to research papers on some
topic. The goal of our project was to create a single search
system, by extending Inquirus [12, 13], capable of producing
meaningful results tailored for each specific need. Unlike
typical recommender systems, we do not have a local
database. Rather, we use the web as our virtual database.
Inquirus, a metasearch engine, sends user queries to over a
dozen different Internet search engines and combines the results
based on the predicted relevance to the given query.
Inquirus 2 extends the notion of relevance to include user
preferences. As a result, different researchers with the same
query will receive personalized recommendations from a
search strategy consistent with their need.
User preferences affect three parts of the search process,
described in detail in Section 2: the sources used, modifications
to the query, and the ordering policy for the results.
A search strategy refers to the collection of these three decisions,
and should be consistent with the stated user need.
For example, a user looking for current events might prefer
more recent documents to older ones, whereas a user looking
for organizational homepages might prefer web pages
with a shorter pathlength (i.e., top of a site’s path) to those
farther down the tree. Likewise a user searching for current
events would likely search a news specific site, whereas a
user looking for company homepages might not. To capture
this, users specify an information need category in addition
to a keyword query. The selection of the information need
category determines the search strategy, which includes an
associated utility function that determines how to score the
results. Every user can have their own personal set of information
need categories (and associated search strategies), or
can use the “expert” defined categories available to everyone.
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