Most existing programmatic exchanges would have already decided to display what they would have deemed as the most relevant query. But not Ubex.

At the input stage, the next DSP-3 receives all the parameters collected by the tracker on the current query and selects one and only one result from the selected materials. It selects not only the most relevant, but also the most financially effective result, which is beneficial both for publishers and advisers. For this purpose, neural network regression is performed for each user action on the advertising material and a map is created containing the estimated indicators.

At the next stage, the tracker already knows which banner or widget to display and passes the task to one of the thousands of free renderers to build a script / css / images to provide a final reply to the user. The final bundle of js code is returned to the tracker, which in turn returns the result as ready-made advertising material to the visitor of the publisher’s site.

All further user actions, including analytics, clicks and actions with advertising material are further served by the tracker leading it and the AI and neural network base. Thus, we obtain a highly organized, intelligent system consisting of hundreds and thousands of DSPs that make small decisions at every stage of displaying advertising material.

As part of the system, thousands of DSPs will perform regressive estimation of qualitative or quantitative indicators for compliance with a certain result. Each DSP is a neural network. The layer of input parameters consists of normalized per unit indicators. At the output stage, the neural network gives normalized per unit judgments about the set of output properties.

Each node within the hidden layers of the network is a neuron. At the first approximation, the artificial neuron imitates the properties of a biological neuron. At the input stage, an artificial neuron receives a certain number of signals, each of which is the output of another neuron. Each input is multiplied by the corresponding weight, similar to the synaptic force and all products are summed, determining the activation level of the neuron. The input signals correspond to signals that come to the synapses of a biological neuron. Each signal is multiplied by the corresponding weight w1, w2, …, wn, and is sent to the summation block, denoted by Σ. Each weight corresponds to the “strength” of one biological synaptic connection. The summing unit, which corresponds to the body of the biological element, algebraically combines the weighted inputs, creating the output NET.

Thus, due to the use of neural networks, the Ubex algorithm allows for taking into account a variety of factors that affect both the relevance of the advertising display and its economic efficiency. The Ubex algorithm collects data about the profile of each specific user, including their behavior, geography, visiting time, interests, etc., and calculates the probability of a targeted action for each individual advertisement. The more data passes through the Ubex neural networks, the more effectively they solve their tasks.

By combining the most advanced technologies in in neuromation, Ubex is creating a unique platform aimed at maximizing profits, minimizing waste and turning the costly process of buying advertising into an immediate decision of selecting target audiences, benefiting all participants of the advertising cycle.