Introduction

Ubiquitous computing environments are those in which interaction with technology occurs at any time and place, sometimes in an invisible way for the user, through any type of device. Multiple technological advances favor the appearance of these environments, such as those related to home automation, wearables or smart cities, in addition to the enormous growth in the use of portable devices such as smartphones or tablets.

All this interaction produces large volumes of data that can be exploited to extract and understand user habits and behaviors. And the acquisition of this knowledge can allow the technology to evolve according to the needs of the user.

Context

Among the many possible ubiquitous contexts, the broadest and most entrenched is the one that arises from interaction through smartphones and mobile applications (or apps). These devices, already in daily use, collect a quantity of data of a varied nature, from the use of user interfaces to geolocation histories or NFC (Near Field Communication) interaction.

The registered information has a versatile richness, from which a large number of usability patterns, mobility and preferences can be extracted, and with the potential to inform about the characteristics of a community of users, feed recommendation systems or adapt the software to an audience. objective. However, special treatment is required for effective knowledge discovery given the high dimensions and constant growth of data.

Project Objectives

The objective of this project is to take advantage of the user patterns that appear in these mobile environments. To do this, the use of machine learning and learning by observation (Learning From Observation) techniques is proposed for the creation of behavioral models from the history of interactions.

The models, with predictive and generative capabilities, will collect the user’s patterns and particularities and will subsequently allow, through unsupervised learning, to segment a community of users into different profiles with a series of typical behaviors.

With the techniques developed, it is desired to obtain a software system that generates specifications in natural language about the user profiles of an app. The generated descriptions will provide a competitive advantage to adjust the services and features of an app to its audience or its different sectors, thus seeking a substantial improvement in the user experience. Within this framework, the user, through their normal activity in an app, can have a much more significant role in its progress.