Automatic web content personalization through reinforcement learning

Abstract

This paper deals with the automatic adaptation of Web contents. It is recognized that quite often users need some personalized adaptations to access Web contents. This is more evident when we focus on people with some accessibility needs. Based on the user profile, it is possible to transcode or modify contents (e.g., adapt text fonts) so as to meet the user preferences. The problem is that applying such a kind of transformations to the whole content might significantly alter Web pages that might become unreadable, hence making matters worse. We present a system that employs Web intelligence to perform automatic adaptations on single elements composing a Web page. A reinforcement learning algorithm is utilized to manage user profiles. We evaluate our system through simulation and a real assessment where elderly users where asked to use for a time period our system prototype. Results confirm the feasibility of the proposal.who are equipped with devices with different capabilities, such as tablets, smart-phones, and smart TVs. In this context, both readability and legibility are affected by different issues, such as Web text characteristics [4,5,6] and users' abilities [7,8,9]. This work presents a system, called ExTraS (EXperiential TRAnscoding System [1,10,11]), and thought to improve Web content legibility. The approach adapts some characteristics, in particular the text formatting ones (e.g. font size, font face, luminance contrasts), according to users' preferences and needs. ExTraS tracks users' behavior to learn and model their preferences and to automatically provide the best adaptation, tailored for each user, predicting his/her needs. Such an adaptation is based on a machine learning mechanism (Reinforcement Learning), exploiting the idea of reward/punishment. We have built a prototype system and we have evaluated it by means of a simulation assessment and tests with real users. Both of them confirm the viability of our proposal.The remainder of the paper is organized as follows. Section 2 describes background and related work, while Section 3 presents the architecture of our system, detailing how it profiles users, how it applies the machine learning mechanism and how it adapts the content. Section 4 introduces the prototype we have developed, showing some screenshots. Section 5 reports the simulation assessment and the tests with users we have performed, detailed the obtained results. Finally, Section 6 concludes the paper illustrating main findings and further work. Background and related workOur work takes into account several issues and has been based on many related work. This section aims to briefly describe the most significant ones, which are related to improving Web pages legibility with font adaptation, adaptation and personalization of digital and Web contents and services, and the use of machine learning techniques and algorithms to track and understand users' experiences and behaviors.According to the definitions in the literature, legibility is related to perceiving text by distinguishing e...

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