Semantic content ranking through collaborative and content clustering

COSMOS-7 models semantic content in MPEG-7 such as objects and events and their spatio-temporality. When a user queries a COSMOS-7 model, the output is usually presented as a sequence of relevant, albeit unranked, video segments through which the user must sift. In this paper, we report how we use Self-Organising Neural Networks (SONNs) to cluster and rank the video segments through consideration of user preferences and knowledge gained from usage of the same content by similar users and of similar content by the same user.