SOI Based Video Recommender Systems - Interaction Design Issues and Collective Intelligence

Recommender systems help users to cope with information overload and have become one of the most powerful and popular tools in electronic commerce. In order to provide better recommendations and to be able to use recommender systems in arguably more complex types of applications, most of the typical used approaches need significant extensions. On the video recommendation domain, one of these extensions is based in Segments of Interest (SOI), i.e., video segments that the user liked more or is interested. For this work, our intention is to stress and discuss interaction design issues about SOI based video recommender systems and discuss the relation between SOI and collective intelligence. We present two approaches to marking SOI on a Web social environment and discuss their advantages and disadvantages, and we show why SOI can be seen as a source of collective intelligence and that information and knowledge emerged from a community that had marked SOIs can be used on consensus decision-making and to bring improvements to society. Author