  It has been shown that relevance judgment of documents is influenced by
multiple factors beyond topicality. Some multidimensional user relevance models
(MURM) proposed in literature have investigated the impact of different
dimensions of relevance on user judgment. Our hypothesis is that a user might
give more importance to certain relevance dimensions in a session which might
change dynamically as the session progresses. This motivates the need to
capture the weights of different relevance dimensions using feedback and build
a model to rank documents for subsequent queries according to these weights. We
propose a geometric model inspired by the mathematical framework of Quantum
theory to capture the user's importance given to each dimension of relevance
and test our hypothesis on data from a web search engine and TREC Session
track.
