# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
###### Abstract
Since the interactivity of IPTV systems and similar video-content-based platforms has been significantly developed in the past years, it has also brought along challenges for usability studies on IPTV systems to improve the quality of the user interfaces. Besides, the interactions between users and IPTV systems take various shapes depending on the source of data. Ref. [9] has conducted a case study on an IPTV app, testing the usability of the system. The focus of the study is on optimising the UI/UX. They provide a solution to define and evaluate the usability problems of the app by investigating remote control data in terms of the interaction between users and IPTV systems. However, there are fewer studies on customers' engagement with IPTV interfaces based on their eye movements. Given that the data behind those IPTV platforms are already being put to use to improve the user's viewing experience, it follows logically that the companies behind these IPTV services would also try to find other methods to improve and enrich their platform in any way they can. In that regard, eye-tracking technology has been introduced in the study of interactions between TV users and the IPTV system regarding gaze movement. The concept of eye tracking has rapid improvement in recent times with advancements in technology, and therefore so has our analysis of the data. Our eyes analyse the world at an almost constant rate; therefore, it makes sense that this information should be used to improve our daily lives. There are potentially many uses for eye tracking technology in multiple areas, to improve professional performance in the workplace, to aid scientific research, and for the Gaming and Virtual Reality (VR) industry, eye tracking can also vastly improve their research. The study in [10] introduces a list of eye-tracking technology implementation methods, including single and multi-camera eye trackers. Eye tracking can provide insight into behaviours and interactions with on-screen content and the surrounding environment [11]. The process through which humans make observations on multimedia content can be analysed via eye tracking [12], which is relevant for IPTV settings. In addition, ref. [13] presents a method for evaluating interface usability based on eye movement data. Both [14; 15] of the more recent studies [14; 15] use eye-tracking equipment to analyse viewing behaviours. Ref. [14] provides a better representation of web pages by combining the user's viewport screenshots with fixed elements on the web page, subsequently assessing the usability of the web page. Ref. [15] utilises eye tracking technology in modelling the eye movement of two different groups of learners reading the same material on the screen to assess their learning experience. In this paper, we focus on the application of eye-tracking technology for the benefit of the Human-Computer Interaction (HCI) study, especially in the IPTV domain. Previously, at the BT Ireland Innovation Centre (BTIIC) in Belfast [11], an eye tracker (Tobii X60) was used to evaluate the user experience on the BT Player, a video-on-demand application supplied by BT as part of their IPTV service in the United Kingdom. The collaboration between BT and Ulster University through the BT Ireland Innovation Centre (BTIIC) has allowed eye-tracking technology to be applied in a typical user setting of the BT Player application and, subsequently, the data from these experiments can potentially offer additional insights into user participation that have not been accessed before. The contributions of this paper can be generalized as follows. Firstly, the paper presents a follow-up investigation of the experimental results by extending our early-stage work [16]. We here apply a first-order discrete-time Markov Chain (DTMC) [17] to model and describe the data generated by the eye tracker, as well as gain insights about customers' interaction with BT Player in terms of their gaze movement.