Towards real-time collaborative filtering for big fast data

The Web of people is highly dynamic and the life experiences between our on-line and \"real-world\" interactions are increasingly interconnected. For example, users engaged in the Social Web more and more rely upon continuous social streams for real-time access to information and fresh knowledge about current affairs. However, given the deluge of data items, it is a challenge for individuals to find relevant and appropriately ranked information at the right time. Having Twitter as test bed, we tackle this information overload problem by following an online collaborative approach. That is, we go beyond the general perspective of information finding in Twitter, that asks: \"What is happening right now?\", towards an individual user perspective, and ask: \"What is interesting to me right now within the social media stream?\". In this paper, we review our recently proposed online collaborative filtering algorithms and outline potential research directions.