User Experience (UX) research teams following a user centered design approach harness personas to better understand a user's workflow by examining that user's behavior, goals, needs, wants, and frustrations. To create target personas these researchers rely on workflow data from surveys, self-reports, interviews, and user observation. However, this data not directly related to user behavior, weakly reflects a user's actual workflow in the product, is costly to collect, is limited to a few hundred responses, and is outdated as soon as a persona's workflows evolve. To address these limitations we present a quantitative bottom-up data-driven approach to create personas. First, we directly incorporate user behavior via clicks gathered automatically from telemetry data related to the actual product use in the field; since the data collection is automatic it is also cost effective. Next, we aggregate 3.5 million clicks from 2400 users into 39,000 clickstreams and then structure them into 10 workflows via hierarchical clustering; we thus base our personas on a large data sample. Finally, we use mixed models, a statistical approach that incorporates these clustered workflows to create five representative personas; updating our mixed model ensures that these personas remain current. We also validated these personas with our product's user behavior experts to ensure that workflows and the persona goals represent actual product use.

We investigate whether XCS, a genetic algorithm based learning classifier system, can harness information from a user's environment to help desktop applications better personalize themselves to individual users. Specifically, we evaluate XCSs ability to predict user-preferred actions for a calendar and a media player. Results from three real-world user studies indicate that XCS significantly outperforms a decision-tree learner to successfully predict user preferences for these two desktop interfaces. Our results also show that removing external user-related contextual information degrades XCSs performance. This performance degradation emphasizes the need for desktop applications to access external contextual information to better learn user preferences. Our results highlight the potential for a learning classifier systems based approach for personalizing desktop applications to improve the quality of human-computer interaction.

We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and we compute an individual's fitness from a weighted combination of user input and user interface design guidelines. Results from our preliminary study involving three users indicate that users are able to effectively bias evolution towards user interface designs that reflect both user preferences and computed guideline metrics. Furthermore, we can reduce fatigue, defined by the number of choices needing to be made by the human designer, by doing two things. First, asking the user to pick just two (the best and worst) user interfaces from among a subset of nine shown. Second, asking the user to make the choice once every t generations, instead of every single generation. Our goal is to provide interface designers with an interactive tool that can be used to explore innovation and creativity in the design space of user interfaces.

We outline our context learning framework that harnesses information from a user's environment to learn user preferences for application actions. Within this framework, we employ XCS in a real world application for personalizing user-interface actions to individual users. Sycophant, our context aware calendaring application and research test-bed, uses XCS to adaptively generate user-preferred alarms for ten users in our study. Our results show that XCS' alarm prediction performance equals or surpasses the performance of One-R and a decision tree algorithm for all the users. XCS' average performance is close to $90$ percent on the alarm prediction task for all ten users. These encouraging results further highlight the feasibility of using XCS for predictive data mining tasks and the promise of a classifier systems based approach to personalize user interfaces.

We investigate the trade off between investing effort in improving the features of a research environment that increases productivity and investing such effort in actually conducting the research experiments using a less elaborated, albeit sufficiently operational environment. The study case presented is an interactive genetic algorithm environment we created to evolve user interfaces designs. We present three productivity improvements integrated in our environment and examine whether on the long run the research productivity can be in fact increased by spending development time on enhancing the research tools rather than on performing the research itself. The three improvements are the integration of the entire system interface into a main wxPython window, the addition of a runs manager for setting up multiple experiments, and the creation of a data manager for effective exploration and visualization of data produced in the experiment runs. We also discuss several guidelines for transitioning a research environment such as ours from a researcher's tool to an end-user's tool.

Research in context-aware user interfaces aims to improve human-computer interaction by providing more effective, smarter and user-friendlier solutions for computer applications. Currently, software available for performing such research and developing context-aware interfaces is very limited both in scope and possibilities of extension. Sycophant was designed with two objectives in mind: first, to allow easy insertion of new features and capabilities needed for conducting research and, second, to provide a reusable, readily available programming resource for developing new context-aware interactive software applications. Available as open source software, Sycophant's API and the calendaring application we created using it are presented in this paper in terms of functional capabilities, high level architecture, detailed design, and results of use. Procedural steps for developing new context-aware user interfaces using our API are also described in the paper.

We present results from an empirical user-study with ten users which investigates if information from a user's environment helps a user interface to personalize itself to individual users to better meet usability goals and improve user-experience. In our research we use a microphone and a web-camera to collect this information (user-context) from the vicinity of a subject's desktop computer. Sycophant, our context-aware calendaring application and research test-bed uses machine learning techniques to successfully predict a user-preferred alarm type. Discounting user identity and motion information significantly degrades Sycophant's performance on the alarm prediction task. Our user study emphasizes the need for user-context for personalizable user interfaces which can better meet effectiveness and utility usability goals. Results from our study further demonstrate that contextual information helps adaptive interfaces to improve user-experience.

Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application program which is designed to learn a mapping from user-related contextual features to reminder actions. In this paper, we consider the feasibility of using a genetics-based machine learning technique, XCS, for the purpose of learning this mapping from a set of context features to reminder actions as a predictive data-mining task. We compare XCS's performance with a decision tree algorithm on this learning task and show that XCS outperforms the decision tree learner.