Academic Research on User Experience (UX)

• User experience-a research agenda, Hassenzahl, M., & Tractinsky, N. (2006). Behaviour & information technology, 25(2), 91-97. This introduction to the special issue of Empirical studies of the user experience seeks to define what is meant by the user experience and provides a brief overview of the user experience. It attempts to outline the future of the research on the user experience, not as a forecast but as a proposal that stimulates the research.
• Understanding, scoping and defining user experience: a survey approach, Law, E. L. C., Roto, V., Hassenzahl, M., Vermeeren, A. P., & Kort, J. (2009, April). This paper conducts a survey among 275 researchers and practitioners from academia and industry to collect their views on user experience (UX). Most of the respondents expressed that the UX is dynamic, subjective and depends on the context. The paper proposes that the UX should be defined as individual emotions (and not social) aroused from interacting with a product, service or system. The findings of the survey support the draft ISO definition, but further explanation is needed on the issues of experiencing anticipated use and the object of UX. The findings of the survey help to understand the concept of user experience and to comprehend the scopes. 
• User experience over time, Karapanos, E. (2013). In Modeling users' experiences with interactive systems (pp. 57-83). Springer, Berlin, Heidelberg. The authors of the paper conducted a five-week-long in-depth ethnographic study on six individuals. The study followed these individuals while they were purchasing the Apple iPhone. The study finds the initial experiences of user with a product relate mostly to hedonic aspects and the prolonged experiences are more tied to aspects reflecting the value of the product in ones life. The paper proposes three directions for Computer-Human Interaction practice: the product needs to be designed for meaningful mediations, it should be attached to daily routine and it should be designed for the self. 
• User experience (UX): towards an experiential perspective on product quality, Hassenzahl, M. (2008, September). In Proceedings of the 20th Conference on l'Interaction Homme-Machine (pp. 11-15). ACM. This paper presents the authors view on User Experience (UX) and discusses its implications for the field of Human-Computer Interaction.
• Construction and evaluation of a user experience questionnaire, Laugwitz, B., Held, T., & Schrepp, M. (2008, November). In Symposium of the Austrian HCI and Usability Engineering Group (pp. 63-76). Springer, Berlin, Heidelberg. The goal of the reported construction process was to develop an end-user questionnaire that can be used for measuring user experience quickly and in a simple and immediate way. At the same time, the aim was to make it comprehensive enough to get a complete impression of the product user experience. It selected the items for the study which have practical relevance. 
• Co-experience: user experience as interaction, Battarbee, K., & Koskinen, I. (2005). CoDesign, 1(1), 5-18. This paper reviews different approaches that are used for understanding the user experience and compares the main three approaches. The paper argues that all of these three approaches lack one perspective, all the approaches focus on the individual experiences and do not pay any attention to the experiences that are created together. The paper presents a new concept called co-experience to address this lacking. This new framework is discussed and illustrated with the help of the data collected from a study on mobile multimedia messaging.
• Recommender systems: from algorithms to user experience, Konstan, J. A., & Riedl, J. (2012). User modeling and user-adapted interaction, 22(1-2), 101-123. This article assesses the progress made in the field of collaborative filtering recommender systems. In this review, it focuses on the evolution from research concentrated purely on algorithms to research based on the questions around the user experience with the recommender. It shows how embedding the algorithm in the user experience affects the value to the user of the recommender. This paper suggests additional measures are required for evaluating the user experiences of a recommender and recommends some effective measures. The paper identifies the most important open research problem and the key challenges that restrict the advancement of the state of the art.
• Explaining the user experience of recommender systems, Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). User Modeling and User-Adapted Interaction, 22(4-5), 441-504. This paper presents a framework to include a user-centric approach in recommender system evaluation. In this framework, objective system aspects are linked to objective user behavior by using a series of the perceptual and evaluative construct. It further includes the influence of personal and situational characteristics on the user experience. The paper identifies several gaps in the existing literature by reviewing how they map to the framework. Four field trials and two controlled experiments are conducted to validate the framework and Structural Equation Modeling is used to analyze it. 
• Needs, affect, and interactive productsFacets of user experience, Hassenzahl, M., Diefenbach, S., & Gritz, A. (2010). Interacting with computers, 22(5), 353-362. This study assumed, pleasurable user experience comes from the universal psychological needs such as competence, popularity, stimulation, relatedness security meaning, or autonomy. To test this assumption, the study collected 500 positive experiences with interactive products (such as mobile phones, computers) and found that a positive relationship indeed exists between need fulfillment and positive effect. Stimulation, relatedness, popularity, and competence were found to be the key needs. The results show the need fulfillment is directly linked to hedonic quality perception, but not as strongly to pragmatic quality. This finding supports the notion that hedonic quality is the motivator and pragmatic quality is the hygiene factor. 
• User experience evaluation methods: current state and development needs, Vermeeren, A. P., Law, E. L. C., Roto, V., Obrist, M., Hoonhout, J., & Vnnen-Vainio-Mattila, K. (2010, October). This paper collected 96 methods of user experience evaluation from academia and industry with different approaches including literature review, workshops, Special Interest Group sessions, and an online survey. These methods are analyzed, mainly based on the product development phase and the studied period of experience. The results identify certain development needs for user experience evaluation methods that include early-stage methods, social and collaborative user experience evaluation method, establishing practicability and scientific quality and a more profound understanding of user experience. 
• Towards a shared definition of user experience, Law, E., Roto, V., Vermeeren, A. P., Kort, J., & Hassenzahl, M. (2008, April). ACM. This paper systematically assembles different existing definitions and perspective on user experience and collects opinions on them from the experts and researchers who are known for working on the UX. It also collects the views of general CHI08 attendees on these definitions and perspectives. 
• A collaborative filtering algorithm and evaluation metric that accurately model the user experience, McLaughlin, M. R., & Herlocker, J. L. (2004, July). This article identifies gaps in two of the most acclaimed Collaborative Filtering (CF) recommendation algorithm that leads to dramatically unacceptable user experience. The article develops a new Belief Distribution Algorithm that overcomes these gaps and provides a considerably richer user modeling. While retaining the qualities of well-performing nearest-neighbor algorithms, this new algorithm produces predictions of belief distributions across rating values rather than a point rating value. The paper argues, these gaps were not revealed for so long because of the exclusive use of the mean absolute error metric. It proposes the use of a modified Precision metric to get more accurate results on user experience.