Exploiting contextual independencies in Web search and user profiling

Abstract

Several researchers have suggested that Bayesian networks be used in web search and user profiling. One advantage of this approach is that Bayesian networks are more general than the probabilistic models previously used in information retrieval. In practice, experimental results demonstrate the effectiveness the modern Bayesian network approach. On the other hand, since Bayesian networks are defined solely upon the notion of probabilistic conditional independence, these encouraging results do not take advantage of the more general probabilistic independencies recently proposed. In this paper, we show how to exploit contextual independencies in both web search and user profiling. Whereas a conditional independence must hold over all contexts, a contextual independence need only hold for one particular context. For web search applications, it is shown how contextual independencies can be modeled using multiple Bayesian networks. We also point to a more general learning approach for user profiling applications.