Action modeling: language models that predict query behavior

We present a novel language modeling approach to capturing the query reformulation behavior of Web search users. Based on a framework that categorizes eight different types of “user moves” (adding/removing query terms, etc.), we treat search sessions as sequence data and build n-gram language models to capture user behavior. We evaluated our models in a prediction task. The results suggest that useful patterns of activity can be extracted from user histories. Furthermore, by examining prediction performance under different order n-gram models, we gained insight into the amount of history/context that is associated with different types of user actions. Our work serves as the basis for more refined user models. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search

• Type: Conference
• Year: 2006
• Where: SIGIR
• Authors: G. Craig Murray, Jimmy J. Lin, Abdur Chowdhury
• Keywords: N-gram Language Models, Query Reformulation Behavior, SIGIR 2006, User