Understanding and modeling behavior patterns in cross-device web search

Recent studies have witnessed an increasing popularity of cross-device web search, in which a user resumes a previously started search task from one device to a later session on another. The complexity of this novel search mode, mainly due to the involvement of interruptions and multiple devices, makes users conduct new types of search behaviors and adopt novel search patterns. Though past research has studied this new search mode, there still lacks a sufficient understanding of cross-device search behaviors. To better understand users' behavioral patterns, we adopted hidden Markov model (HMM) to model observed cross-device search behaviors and their underlying search pat- terns under the same framework. HMM is a widely adopted machine learning technique with the main assumption that observations (e.g., observed search behaviors) are driven by hidden variables (e.g., hidden behavioral patterns). Utilizing the data collected from a user study consisting of two types of cross-session search conditions – mobile-to-desktop (M-D) and desktop-to-desktop (D-D) – we demonstrate the validity of using HMM for modeling and identifying hidden behavioral patterns, and based on the identified patterns, we discover that re-finding is a common pattern at the beginning of the continued search session, and querying, exploitation and exploration contribute to the dominated behaviors in later search stages. Our results also show clear device effects on cross-device search (M-D) vs cross-session search (D-D). All these evidences confirm that better support mechanisms are in critical need for a cross-device search and the HMM- based modeling can help.