Early prediction in collective intelligence on video users’ activity

The huge volume of available video content calls for methods that offer insight to the content without necessitating burdensome users’ extra effort or being applicable to specific types or conditions. Based on experimentation on collective users’ interactions on a controlled user-experiment, this work analyses the results collected following the argument that bell-shaped reference patterns are shown to significantly correlate with scenes of interest for each video, as designated by the viewers. Though, in order to ensure the correlation of results to bell-shaped reference patterns, aggregation of a number of users’ interactions is required. In order to overcome such an impediment and adhere to a real-case cold start scenario, we propose a stochastic transformation of the aggregated users’ interaction signal into a space defined by its correlation to the bell-shaped reference patterns that is shown to offer significant amelioration as to the percentage of users’ interaction required in order to achieve comparable results to the original users’ interaction space. Moreover, to ensure further the realistic character of the proposed scenario, given an amount of already collected users’ interaction, the interaction of new users’ is shown to be predictable using neural network time series prediction and modeling methods. The results received indicate increased accuracy on how one can predict the most important scenes from low quantity early data of users’ interactions as well as future interaction of unique users. In practice, the proposed techniques might improve both navigation within videos on the web as well as video search results with personalised video thumbnails.