Modeling user context with applications to media retrieval

In this paper, we develop a theoretical understanding of multi-sensory knowledge and user context and their inter-relationships. This is used to develop a generic representation framework for multi-sensory knowledge and context. A representation framework for context can have a significant impact on media applications that dynamically adapt to user needs. There are three key contributions of this work: (a) theoretical analysis, (b) representation framework and (c) experimental validation. Knowledge is understood to be a dynamic set of multi-sensory facts with three key properties - multi-sensory, emergent and dynamic. Context is the dynamic subset of knowledge that affects the communication between entities. We develop a graph based, multi-relational representation framework for knowledge, and model its temporal dynamics using a linear dynamical system. Our approach results in a stable and convergent system. We applied our representation framework to a image retrieval system with a large collection of photographs from everyday events. Our experimental validation with the retrieval evaluated against two reference algorithms indicates that our context based approach provides significant gains in real-world usage scenarios.