Using eye movement sequences and hidden Markov model to explore strategy blending in metadata-supported image search

Abstract

Abstract This study explores users’ search strategies associated with different information aids in an image search context. We investigate "strategy blending", i.e., the mixture of different strategies (tag-based and header-based) in a display with both possibilities. Using eye-movement-recordings, supported by Hidden Markov Model (HMM) modeling, we attempt to reveal strategies and tactics as well as the blending of different strategies. The findings make theoretical contributions to the literature on strategy blending and information seeking behavior and provide practitioners with guidelines on metadata support for website design to enhance the user experience and meet user needs. In our understanding of this domain, we are the first to bridge strategies in decision making to search strategies with actual users instead of using mere simulation. Strategy blending can be explained by investigating user search behavior in an image search context. HMMs can be used to discover latent search tactics, and user eye movement sequences reveal different types of strategy blending. For practical design in the context of image search, metadata is indeed useful to assist image search as a navigation support, and represents a better chance to fulfill the information needs of users. Responding to the emerging digital environment and the new paradigm of people’s search behavior with various devices, these results can be generalized to other research fields, such as mobile system design or user modeling, to satisfy various users with different needs.