| dc.description.abstract |
The current search engines available on the Net are generic in nature . They do
not consider user preferences and treat all users information needs in the same way .
As a result they frequently return a large number of links , that do not meet the user's information need . This requires more searching to find what the user is looking for .
For example if a user is interested in a particular game , e .g . cricket , and enters the
query world cup , a generic search engine would return links of all the sports that hold a
world cup . The user has to browse through a considerable number of non -relevant pages
before he is able to get to links he is looking for . This is also because search engines
don't have the ability to ask a few questions and they also can not rely on judgment
and past experience to rank web pages , in the way humans can . This raises the issue of
customizing a generic search engine to consider user preferences .
There have been a number of attempts in the past to personalize the search for
information on the Net . These systems are based on relevance feedback methods , similarity measures , or storing a user profile explicitly or implicitly . Some of them have shown
impressive results in query expansion and providing pages similar to the user's interest .
Here we propose a novel system for personalizing web search . Our method is based
on creating a user profile as he performs his routine searches in a given user category . In
this customization , the user is allowed to create personal user categories within which he
could search for information on the Net without getting too many irrelevant links in his
search results . The application enhances the query by adding words that are generated
from the user profile stored for a particular user category . It uses information about the
probabilistic co -occurrence of words in the user profile with other words in the query as
a measure for adding words . The snippets returned from the generic search engine are
then classified on the basis of the user profile and are re -ordered according to a measure representing the interest of the user . With our method the user not only gets personalized query expansion but also
receives re -ordered search results from the search engine . In this system the query expansion is made to optimize the estimated returns of the search engine , taking into account the classification accuracy and re -ranking of results . The system add words that would give the user the best possible returns according to his user profile . |
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