# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
###### Abstract  
Human-Computer Interaction (HCI) research has extensively employed eye-tracking technologies in a variety of fields. Meanwhile, the ongoing development of Internet Protocol TV (IPTV) has significantly enriched the TV customer experience, which is of great interest to researchers across academia and industry. A previous study was carried out at the BT Ireland Innovation Centre (BTIIC), where an eye tracker was employed to record user interactions with a Video-on-Demand (VoD) application, the BT Player. This paper is a complementary and subsequent study of the analysis of eye-tracking data in our previously published introductory paper. Here, we propose a method for integrating layout information from the BT Player with mining the process of customer eye movement on the screen, thereby generating HCI and Industry-relevant insights regarding user experience. We incorporate a popular Machine Learning model, a discrete-time Markov Chain (DTMC), into our methodology, as the eye tracker records each gaze movement at a particular frequency, which is a good example of discrete-time sequences. The Markov Model is found suitable for our study, and it helps to reveal characteristics of the gaze movement as well as the user interface (UI) design on the VoD application by interpreting transition matrices, first passage time, proposed'most likely trajectory' and other Markov properties of the model. Additionally, the study has revealed numerous promising areas for future research. And the code involved in this study is open access on GitHub. e 2023 16 82 2023  
In the past, the TV experience involved a fixed number of channels showing one programme at a given time, with the customer only having the choice of a limited variety of channels [3]. Today, however, this linear approach has been totally redeveloped with the use of IPTV services. This advancement in technology has not only improved the viewing experience, but it provides more detailed data to analyse, and this, in turn, can ultimately improve the customer's interaction with the service. For instance, we have seen on IPTV platforms how algorithms are used to predict other films or TV shows that the customer may be interested in based on their viewing history. For many, this is a welcome development, as it reduces the need to scroll through significant numbers of pages in an attempt to search for a film, particularly if the user is simply \"browsing\" and has no specific film in mind to watch. As a consequence, it has been a trending research topic for IPTV service providers or video-sharing platforms to gain insights into users' interaction data regarding their preferences for programme content, habits of using the Electronic Program Guide (EPG), patterns of TV viewing time on a daily/weekly/monthly basis, or quality of the service, etc. In the meantime, analytics technologies have been widely used for data provided by a variety of devices and viewing media for industrial and academic study in order to get a deeper knowledge of TV viewers' behaviour [4]. In the IPTV domain, studies of customer behaviour normally fall into one of the following categories: One is related to how they interact with the TV service plan and Electronic Programme Guide (EPG) provided by the IPTV service provider. Such actions can be briefly described as \"subscribe to the service,\" \"turn on the set-top-box,\" \"use the remote control,\" \"watch a programme,\" \"switch channels,\" \"add a programme to favourites,\" \"purchase a programme,\" \"unsubscribe to the package,\" \"leave the service,\" etc. The other direction of study is more focused on customers' preferences concerning programme content and genre when they are watching IPTV. Respectively, there have been studies related to IPTV customers, including behaviour prediction (e.g., churn prediction [5], channel switch prediction [6; 7]) and recommender systems [8]).