Mobile applications development has been enhanced by Machine Learning (ML) and Artificial Intelligence (AI). The integration of ML models in applications in order to classify or predict events allows the creation of applications able to understand and recognise user’s behaviour and make their experience more intelligent and interesting. An easement to understand example is the next words suggested to user based on the previous content typed while texting messages. So, this article will analyse the integration of a ML model into a mobile flutter application to audio recognition.

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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.