Modeling Multidimensional User Relevance in IR using Vector Spaces
This work addresses the challenge of personalizing search results for users by dynamically adjusting to their shifting preferences during sessions, representing an incremental advance in multidimensional relevance modeling.
The paper tackled the problem of modeling dynamic user relevance in information retrieval by proposing a geometric model based on Quantum theory to capture changing importance of relevance dimensions, achieving improved ranking on web search and TREC Session track data.
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.