Abstract: A system for selecting a video to be inserted into an article displayed at a web page includes a blackboard. The blackboard stores extracted values for operation thereon by one or more workers. An article extractor creates an article feature as a function of information extracted from the article and inputs the article feature to the blackboard. A threshold extractor determines a threshold factor as a function of the relevance of the video to the article, and inputting the threshold factor to the blackboard. A more like this recommender receives the article feature and outputs to the blackboard, as a function of the article feature, a more like this recommendation as a list of videos to be placed in the article. A more like this decider determines, as a function of the threshold factor and the more like this recommendation, which video to select as a more like this decision.

Abstract: A method finds people within a network of people who are interested in the same topic. Individual user profiles, for people within the network of people based upon concepts featured in documents consumed by said people so that the individual user profiles include user-specific concepts, are provided in a database. A document concerning a topic of interest is selected. A computer system creates a model of the selected document including document-specific concepts featured in the selected document. A computer system compares the document-specific concepts to the user-specific concepts from the individual user profiles. Any matches as a result of the comparing step are determined. If there are any matches, at least one match is reported to a user.