Modeling Multidimensional User Relevance in IR using Vector Spaces

TL;DR

This paper introduces a quantum-inspired geometric model to dynamically capture and utilize user relevance dimension weights in information retrieval sessions, improving document ranking based on evolving user preferences.

Contribution

It proposes a novel quantum-inspired geometric framework to model and adapt to changing user relevance dimension importance during search sessions.

Findings

Model effectively captures dynamic relevance dimension weights.

Improves document ranking accuracy in session-based search.

Validated on web search and TREC Session data.

Abstract

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.