Abstract
Diversity in users’ information needs has been effectively dealt with through personalized Web search systems whereby a user’s interests and preferences are taken into account within the retrieval model. A significant component of any Web search personalization model is the means with which to model a user’s interests and preferences to build what is termed as a user profile. This work explores the use of the Twitter microblog network as a source of user profile construction for Web search personalization. We propose a statistical language modeling approach taking into account various aspects of a user’s behavior on the Twitter network (such as Twitterers followed, mentioned and retweeted). The model also incorporates network and topical similarity measures which enables the model to be a better representation of the user’s profile. The richness of the Web search personalization model leads to significant performance improvements in retrieval accuracy.
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