Quantum-like modelling of user interaction and cognition in information seeking

Topic Description

User's online information seeking involves a number of key aspects, including the user’s interaction with the system and with other users, as well as evolution of the user’s cognitive status which underpins the user’s decision making, e.g., on which particular information items are relevant, to be clicked and to be viewed. There has been recent and growing evidence showing that the user cognition and decision making may not always obey the classical Kolmogorovian probability theory (e.g., the law of total probability) and classical logic (e.g., the distributive law), which are theoretically set-based (e.g., set of keywords).

The recent emergence of quantum-like theories (QT) support the argument that quantum-like phenomenon and non-Kolmogorovian probabilities that build on vector subspaces (instead of subsets as in classical theory) exist in human cognition and human decision making, user behaviour and interactions, which are all essential aspects in information retrieval and seeking. In particular, we view QT as an extremely promising theoretical framework to address the research issues in this project that aims to establish a non-classical theoretical foundation to model user interaction and cognition in information seeking. We will exploit the key quantum concepts (such as supposition, entanglement, measurement, interference), and develop a subspace based theoretical framework and correspondingly a non-classical logic to better support user interaction and cognition in information seeking.

Skills Required:

Applicants must have a high quality Honours Degree (preferably First Class) or a Master qualification (preferably with distinction) in computer science, applied mathematics or physics; Knowledge of probability theory and statistics; programming skills; Research experience and publications in information retrieval, natal language processing, human computer interaction, and/or quantum physics would be desirable.

Background Reading:

Busemeyer, J.R. and Bruza, P.D. (2012). Quamtum Models of Cognition and Decision. Cambridge University Press.

Bruza, P.D., Kitto, K., Sitbon, L., Song, D. and Blomberg, S. (2011). Quantum-like non-Separability of Concept Combinations, Emergent Associates and Abduction. Logical Journal of the IGPL. Vol. 20(2), pp. 445-457.

Li, Q., Li, J., Zhang, P., Song, D. (2015). Modeling Multi-query Retrieval Tasks Using Density Matrix Transformation. ACM SIGIR2015, pp. 871-874. August 2015, San Diego, Chile.

Melucci, M. (2013). Deriving a Quantum Information Retrieval Basis. The Computer Journal (2013) 56 (11): 1279-1291.

Van Rijsbergen, C.J. 2004 Geometry of Information Retrieval. Cambridge University Press.

Song, D., Lalmas, M., van Rijsbergen, et al. (2010). How Quantum Theory is Developing the Field of Information Retrieval. AAAI-Fall Symposium on Quantum Informatics for Cognitive, Social, and Semantic Processes (QI2010), pp. 105-108. 11-13 November 2010, Washington DC, USA.

Wang, J., Song, D., Zhang, P., Hou, Y. and Bruza, P.D. (2010). Explanation of Relevance Judgement Discrepancy with Quantum Interference. AAAI-Fall 2010 Symposium on Quantum Informatics for Cognitive, Social, and Semantic Processes (QI2010), pp. 117-124. 11-13 November 2010, Washington DC, USA.

Wang, J., Song, D. and Kaliciak, L. (2010). Tensor Product of Correlated Text and Visual Features: A Quantum Theory Inspired Image Retrieval Framework. AAAI-Fall 2010 Symposium on Quantum Informatics for Cognitive, Social, and Semantic Processes (QI2010), pp. 109-116. 11-13 November 2010, Washington DC, USA.



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