An adaptive contextual quantum language model

User interactions in search system represent a rich source of implicit knowledge about the user’s cognitive state and information need that continuously evolves over time. Despite massive efforts that have been made to exploiting and incorporating this implicit knowledge in information retrieval, it is still a challenge to effectively capture the term dependencies and the user’s dynamic information need (reflected by query modifications) in the context of user interaction. To tackle these issues, motivated by the recent Quantum Language Model (QLM), we develop a QLM based retrieval model for session search, which naturally incorporates the complex term dependencies occurring in user’s historical queries and clicked documents with density matrices. In order to capture the dynamic information within users’ search session, we propose a density matrix transformation framework and further develop an adaptive QLM ranking model. Extensive comparative experiments show the effectiveness of our session quantum language models.