Jerarquización orgánica de registros semánticos para posicionamiento natural

API para comincar con la consola de Google Play

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Many recommendation systems must optimise for multiple objectives at the same time, such as relevance, popularity, or personal preferences. We formulated the multi-objective optimisation problem as a constrained optimisation problem: the overall objective is to maximise the expected value of a primary metric, subject to constraints in terms of expected values of secondary metrics. During online serving, the objectives may shift according to user’s needs – for example, a user that had previously been interested in housing search apps might have found a new flat, and so is now interested in home decor apps – so we worked toward a dynamic solution.

Rather than solving the problem offline and bringing a fixed model online, we solved this problem on-line, per-request, based on the actual values of the objectives during serving time. We define the constraints to be relative constraints, meaning we would like to improve the secondary objective by a percentage rather than an absolute value. This way, any shifts in the secondary objectives didn’t affect our solver.

The algorithm that we developed can be used to find tradeoffs between a number of metrics. Finding suitable points along the tradeoff curve, our algorithm can significantly raise secondary metrics with only minor effects on the primary metric.

Google en el punto de mira del monopolio de la patente.

In this post we are going to learn how to optimize a Google Play Store listing of any Android app in order to increase visibility and volume of organic downloads from the store.

To do App Store Optimization effectively, it is crucial to understand the algorithm used by each app store. Even though, the algorithm, classifying and ranking apps is not public, you will see that some trick in the Google Play Store won’t work in the Apple App Store. Let’s have a look on the most relevant difference: The App Store Algorithm.

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