Personalization and diversification of search results
Date
2013
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
There has been lot of research in the area of information retrieval on different aspects of search such as personalization, diversity, evaluation measures etc. In
this thesis, we hypothesize that personalization and diversification can coincidently
exist with each other. We propose two novel approaches, one for personalization by
incorporating feedback from query logs of similar users by extending the state art of
personalization method, other for subtopic retrieval using N-grams as document representatives for diversity.
There is a general consensus among researchers that personalization and diversity are
opposed to each other since personalization advocates for information based on user
interests while diversity support the maximum information gain for a given query by
selecting documents which incorporate all perspectives of query. Our model aims to
provide the users with maximum diverse information with consideration of user interests. For example, for a given a query "RSS" which has numerous meanings such as
Rich Site Summary, Rashtriya Swayamsevak Sangh, Remote Sensing Service etc., the
proposed system output should accommodate not only different aspects of the query
in the output results, but also consider user interests. Given the above mentioned
query, for users interested in politics, documents with the Rashtriya Swayamsevak
Sangh aspect should be ranked higher compared to documents related to the technical
perspective, i.e., Rich Site Summary.