Modeling Long-Term Search Engine Usage

Search engines are key components in the online world and the choice of search engine is an important determinant of the user experience. In this work we seek to model user behaviors and determine key variables that affect search engine usage. In particular, we study the engine usage behavior of more than ten thousand users over a period of six months and use machine learning techniques to identify key trends in the usage of search engines and their relationship with user satisfaction. We also explore methods to determine indicators that are predictive of user trends and show that accurate predictive user models of search engine usage can be developed. Our findings have implications for users as well as search engine designers and marketers seeking to better understand and retain their users.