Understand the popularity of a source of information better than a human, without bias. This function uses a variety of parameters and a computational model that accounts for the average amount of traffic, individual bounce rate, and average time spent on the source. The popularity score is made more accurate by inputting data that is specific to the source only, factors including the relevancy of the source to the news, social popularity, and influence. By blending all of these data points the individual popularity of a source can be quantified giving you complete transparency on a source’s popularity.
The model considers a wide variety of factors that are dynamically adjusted as part of the feedback process which refines and increases the accuracy of the algorithm
Various regression models are assigned to the various factors which are continuously refined by the Accrete development team improving the scoring over time
Accrete hosts our knowledge functions using global computing infrastructure that is reliable, scalable and secure. Service redundancy is guaranteed with 99.999% uptime.