Towards end-user physiological profiling for video recommendation engines

This paper describes research aimed at creating intelligent video recommendation engines for broadband media services in digital homes. This paper is also relevant to intelligent environments because the physiological techniques described here can be re-used for end-user profiling and better context modelling in intelligent environment applications. The aim of our research is to harness physiological signals to characterise people's video selection preferences which we plan to integrate into new generations of video recommendation engines. We describe an initial experiment aimed at determining whether videos produce useable variations in physiology and linking these with emotional changes elicited by video material. We discuss our results and consider the possibility of utilising physiological sening methods to build profiles that can be treated as signatures. Finally, we conclude by describing the future directions of our work.