Timely and Keyword-Based Dynamic Content Selection for Public Displays

In public display systems determine what to present and when is a central feature. Although several adaptive scheduling alternatives have been explored, which introduce sensibility of the display to some type of external variable, they are still very dependent on the user in their behavior, content specific in their nature and very rigid in their adaptation to their social environment, not providing visitors of the place with appropriate, rich and personalized information according to their interests and expectations. There is a need for solutions that successfully integrate the wealth of dynamic web sources as providers for situated and updated content with social and contextual environment around the display so as to present the most appropriate content at every moment, and thus improving the utility of the system. In this paper, we present a recommender system for public situated displays that is able to autonomously select relevant content from Internet sources using a keyword-based place model as input. Based on external relevance criteria the system finds and pre-selects only those sources that are more relevant, and an adaptive scheduling algorithm continuously select content that are relevant, timely, in accordance with the place model, sensitive to immediate indications of interest and balanced to serve the broad range of interests of the target population. To evaluate this system we have carried out two partial experiments. The results showed that keyword-based shared place models jointly with content specific relevance models are a simple and valid approach to user-generated content for public displays.