A language modeling approach to personalized search based on users' microblog behavior

Personalized Web search offers a promising solution to the task of user-tailored information-seeking, and particularly in cases where the same query may represent diverse information needs. 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 features of a user’s Twitter network. The richness of the Web search personalization model leads to significant performance improvements in retrieval accuracy. Furthermore, the model is extended to include a similarity measure which further improves search engine performance.