International Journal of Computer Applications (0975 – 8887) Volume 130 – No.10, November2015 8 Fuzzy Tier-based User Experience Prediction Scheme Ahmed. A. A. Gad-ElRab Department of Mathematics Faculty of Science Al-Azhar University-Cairo, Egypt Kamal A. ElDahshan Department of Mathematics Faculty of science Al-Azhar University-Cairo, Egypt Mahmoud Embabi Department of Mathematics Faculty of Science Al-Azhar University-Cairo, Egypt ABSTRACT Building professional and efficient systems by using user experience became one of the important research activities that focus on the interactions between products, applications, designers, and users. Unfortunately, using user experience faces many problems. One of these problems is how to predict a user experience efficiently to build robust, effective, and flexible applications. To solve this problem, it is needed to design an optimal and efficient method for predicting user experience which includes behavior and emotions experiences. In this paper, a two-tier ranking scheme by using two multi-criteria decision making approaches is proposed. This proposed scheme considers a user experience as a sequence of executed actions or operations and it can predicate the most efficient user experience sequence of operations among a group of user experiences or experiences of individual users on a certain system or application. It uses the combination of two multi-criteria decision making approaches, the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) in Fuzzy environments to rank each operation or action in a user sequence. Based on operation rank, in the first tier, the proposed algorithm selects all sequential operations with the highest ranks. If there are sub goals are not satisfied in the first tier, then in the second tier, the algorithm ranks all unselected operations and add all operations with the highest ranks which satisfy these sub goals. This new scheme is presented as a flexible and efficient method for predicting user experience which will be help designers and developers in building professional systems and applications. Keywords Human computer interaction, User experience design, Fuzzy sets, AHP, TOPSIS. 1. INTRODUCTION Designing a user experience (UX) became a critical issue for building professional and efficient systems due to the development of information technology schemes, HCI techniques and electronic devices. The user experience introduces new research activities that focused on the interactions between products, applications, designers, and users. Recently, a lot of industrial and technological companies have touched the importance of UX as a key success issue in product design [1]. The creating meaningful UX is not just usability but it goes far more. Therefore, it is essential to take into account other cognitive, socio-cognitive, and affective aspects of UX in the interaction process, such as users’ enjoyment, brand loyalty, mental models, and aesthetic experience [2]. In addition, the user behavior is very important issue to be considered in designing UX. In product design process, there are many interdependent designing attributes are considered as a consistent whole to create unique UX, especially to achieve valuable higher economic benefit and customer desires [3]. The evolution of user's emotional states and cognitive processes with choice decision making are the chain of human-product interactions [4]. Traditionally, most of designers concentrated on functional requirements for physical products and did not consider users' behavior and affective and cognitive needs. Recently, designers can utilize the new technologies, compose multimedia platforms with services, or use of sensory information for creating meaningful UX based on the context of work environments [5]. In decision making, human emotional experience plays a significant role towards product success [6]. Therefore, it is very important to consider the human dimension in design research [7]. Most of existing human decision making systems have been well addressed based on the user cognitive experiences. However, these systems miss the affective elements for modeling, analyzing and simulating human realization on UX in the predominant computational models [8]. Recent models based on behavioral decision theories focus on cognitive errors and heuristics in human decision making, but still ignore the role of emotion in human decision making [9, 10]. Users' affective states often influence their experience at the time of decision making, so a single cognitive perspective is not optimal for analyzing human decisions for meaningful UX [10]. Recently, in [11] the integration of emotion and cognition has been driven by the intimate coupling of affective and cognitive decisions. To meet user goals, in this paper, a two-tier ranking scheme based on fuzzy decision making approaches and category and activity theories is proposed. This proposed scheme considers a user experience as a sequence of executed actions or operations and it can predicate a most efficient user experience sequence of operations among a group of user experiences or experiences of individual users on a certain system or application. It uses the combination of two multi- criteria decision making approaches, the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS) in Fuzzy environments to rank each operation in a user sequence. Based on operation rank, in the first tier, the proposed algorithm selects the all sequential operations with the highest ranks. If there are sub goals are not satisfied in the first tier, then in the second tier, the algorithm ranks all unselected operations and add all operations with the highest ranks which satisfy these sub goals. This new scheme is presented as a flexible and efficient method for predicting user experience which will be help designers and developers in building professional systems and applications. The rest of the paper is organized as the following. Section 2 includes a detailed survey of the related work. Section 3 introduces multi-criteria decision making approaches. Section 4 describes user experience prediction problem and its related assumptions. Section 5 describes a proposed user experience prediction scheme. Section 6 presents real scenario example and its analysis results. Finally, Section 7 concludes the paper.