Exploring personality-targeted UI design in online social participation systems

We present a theoretical foundation and empirical findings demonstrating the effectiveness of personality-targeted design. Much like a medical treatment applied to a person based on his specific genetic profile, we argue that theory-driven, personality-targeted UI design can be more effective than design applied to the entire population. The empirical exploration focused on two settings, two populations and two personality traits: Study 1 shows that users' extroversion level moderates the relationship between the UI cue of audience size and users' contribution. Study 2 demonstrates that the effectiveness of social anchors in encouraging online contributions depends on users' level of emotional stability. Taken together, the findings demonstrate the potential and robustness of the interactionist approach to UI design. The findings contribute to the HCI community, and in particular to designers of social systems, by providing guidelines to targeted design that can increase online participation. Copyright © 2013 ACM

Product Configuration with Bayesian Network

For the satisfaction of individual customer requirements, products with many options are offered in mass customization. However, in the area of ecommerce, the large number of possible product configurations can overwhelm the customer, as he or she is not supported by a human sales expert. To minimize the customer’s overload, this paper examines the combination of a knowledge-based product configurator with an upstream probabilistic recommender system to provide a quick, individual and dynamic initial orientation for the customer. The application of the approach is demonstrated using an example from engineering design

Automating Software Customization via Crowdsourcing using Association Rule Mining and Markov Decision Processes

As systems grow in size and complexity so do their configuration possibilities. Users of modern systems are easy to be confused and overwhelmed by the amount of choices they need to make in order to fit their systems to their exact needs. In this thesis, we propose a technique to select what information to elicit from the user so that the system can recommend the maximum number of personalized configuration items. Our method is based on constructing configuration elicitation dialogs through utilizing crowd wisdom. A set of configuration preferences in form of association rules is first mined from a crowd configuration data set. Possible configuration elicitation dialogs are then modeled through a Markov Decision Processes (MDPs). Within the model, association rules are used to automatically infer configuration decisions based on knowledge already elicited earlier in the dialog. This way, an MDP solver can search for elicitation strategies which maximize the expected amount of automated decisions, reducing thereby elicitation effort and increasing user confidence of the result. We conclude by reporting results of a case study in which this method is applied to the privacy configuration of Facebook

Ομαδικές συστάσεις βάσει περίπτωσης για διαμορφώσιμα προϊόντα με χρήση πολυδιάστατης ομαδοποίησης

Τα συστήματα συστάσεων παρέχουν εξατομικευμένες προτάσεις στους χρήστες σχετικά με αντικείμενα ή θέματα που εκτιμάται ότι θα τους ενδιαφέρουν. Τα μοντέρνα συστήματα συστάσεων υποστηρίζουν τους χρήστες στην επιλογή αντικειμένων ενός συγκεκριμένου είδους (για παράδειγμα, ταινίες, βιβλία και τραγούδια). Η παρούσα εργασία επικεντρώνεται σε ένα σχετικά νέο τομέα συστημάτων συστάσεων που αφορά τα διαμορφώσιμα προϊόντα (configurable products) τα οποία αποτελούνται από επιμέρους αντικείμενα και προτείνονται στο χρήστη ως σύνολο, όπως είναι για παράδειγμα ένας H/Y. Συνήθως, τα συστήματα συστάσεων επωφελούνται των τεχνικών του συνεργατικού φιλτραρίσματος (ΣΦ) που προβλέπουν αντικείμενα για το νέο χρήστη με βάση τις προτιμήσεις άλλων όμοιων χρηστών. Εκτός από το συνεργατικό φιλτράρισμα, τα συστήματα συστάσεων χρησιμοποιούν επίσης άλλες τεχνικές μηχανικής μάθησης όπως ομαδοποίηση (clustering) και κατηγοριοποίηση (classification) δεδομένων. Η παρούσα διπλωματική εργασία στοχεύει στην πρόταση μιας νέας αποδοτικής τεχνικής συστάσεων ανασχηματιζόμενων προϊόντων για ομάδες χρηστών. Προτείνεται η δημιουργία ενός υβριδικού συστήματος συστάσεων ΣΦ και συστάσεων βάσει περίπτωσης (case-based) το οποίο θα προτείνει διαμορφώσιμα προϊόντα σε ομάδες χρηστών μέσω της υιοθέτησης τεχνικών πολυδιάστατης ομαδοποίησης και κατηγοριοποίησης. Ειδικότερα, χρησιμοποιούμε τα δημογραφικά δεδομένα και τις προτιμήσεις των χρηστών για να τους ομαδοποιήσουμε σε πολλαπλές κατηγορίες και στη συνέχεια δημιουργούμε ένα μοντέλο που εντάσσει το νέο χρήστη σε μία από αυτές. Οι νέοι χρήστες ομαδοποιούνται βάσει κατηγορίας και οι συστάσεις παρέχονται στην ομάδα βάσει των διαμορφώσεων εγγεγραμμένων χρηστών που οι προτιμήσεις τους μοιάζουν περισσότερο με της εκάστοτε ομάδας. Η πειραματική αξιολόγηση αποδεικνύει ότι η ενσωμάτωση της πολυδιάστατης ομαδοποίησης βελτιώνει την ακρίβεια των συστάσεων. Παράλληλα, αντιμετωπίζει τα κυριότερα προβλήματα των τεχνικών ΣΦ που είναι η αραιότητα των αξιολογήσεων και το πρόβλημα της ψυχρής εκκίνησης.Recommender systems provide personalized suggestions to end users regarding items or concepts that they will probably find interesting. Modern recommenders help users to select items of a specific kind, for instance films, books or songs. This thesis focuses on a relatively new field of recommender systems concerning configurable products which consist of individual attributes or parts. These parts are recommended to the user as a whole, for example a customizable PC. Usually, recommenders leverage collaborative filtering methods that predict items for new users based on the preferences of other similar users. Apart from collaborative filtering, recommenders are likely to use other techniques common in data mining such as clustering and classification of data. The aim of this thesis is to propose an effective approach for recommendation of configurable products for groups of users. We suggest and describe the creation of a hybrid collaborative filtering and case-based recommender system, which will propose configurations to groups by applying multidimensional clustering and classification algorithms. Specifically, we use demographic data and users’ preferences to cluster them in multiple classes and then we create the model which classifies the new user into one of these classes. New users are grouped by class and recommendations are provided to each group based on the configurations of registered users whose preferences are more similar to the group’s aggregated preferences. Experimental evaluation of the aforementioned system proves that the integration of multidimensional clustering improves the precision of the recommendations. At the same time, it deals with the major problems of collaborative filtering approaches, which are the sparseness of rankings (for new items) and the cold start problem (for new users)

Determinants of hedonic and utilitarian factors in social network sites acceptance model

Social Network Sites (SNS) are a rapidly growing phenomenon. Despite considerable growth in the number of SNS, very few of these sites are successful at retaining membership and confirming behavioural use intention by their members. At the same time, despite remarkable statistics related to the number of users and rate of growth of successful SNS, there has been little research into an explanation on sources of user acceptance on these sites. In particular, though SNS are found to be both hedonic oriented and utilitarian oriented systems, the combined influence of both hedonic and utilitarian factors on acceptance of SNS has been rarely investigated. The purpose of the study is to identify determinants of hedonic and utilitarian factors leading to SNS user use intention. Through the unification of theoretical backgrounds of behavioural use intention, in particular the Technology Acceptance Model and interdisciplinary literature relevant to SNS, comprehensive set of constructs and their interrelationships were formed as the research hypotheses. The research hypotheses guide the development of measurement model which was specified in an instrument. The instrument was applied in two stages of a pilot study and the main study for data gathering. Employing cluster sampling technique, 712 students of 15 faculties as secondary sampling units from three academic institutes as primary sampling units responded to the study in a paper-based questionnaire mode. The study applied Structural Equation Modeling and statistical analysis such as factor analysis, path analysis and regression analysis. The findings demonstrate the relation between various aspects of utilitarian and hedonic factors with use intention through the representative constructs of Perceived Enjoyment and Perceived Usefulness. As a result, four constructs including Social Connectedness, Social Communication, Social Awareness and Subjective Norms were identified to be determinants of Perceived Usefulness in SNS. On the other hand, Interactivity in Use, Curiosity and Novelty were identified as determinants of Perceived Enjoyment. Additionally, the significant relationships between Perceived Enjoyment and Perceived Usefulness with Behavioural Use Intention on SNS were found. The results lead to development of SNS acceptance model including both significant influential hedonic and utilitarian factors. This study provides a theoretical model and an instrument for evaluating the acceptance of SNS and has the potential to guide the implementation and design of new SNS

Understanding and Improving Continuous Experimentation : From A/B Testing to Continuous Software Optimization

Controlled experiments (i.e. A/B tests) are used by many companies with user-intensive products to improve their software with user data. Some companies adopt an experiment-driven approach to software development with continuous experimentation (CE). With CE, every user-affecting software change is evaluated in an experiment and specialized roles seek out opportunities to experiment with functionality. The goal of the thesis is to describe current practice and support CE in industry. The main contributions are threefold. First, a review of the CE literature on: infrastructure and processes, the problem-solution pairs applied in industry practice, and the benefits and challenges of the practice. Second, a multi-case study with 12 companies to analyze how experimentation is used and why some companies fail to fully realize the benefits of CE. A theory for Factors Affecting Continuous Experimentation (FACE) is constructed to realize this goal. Finally, a toolkit called Constraint Oriented Multi-variate Bandit Optimization (COMBO) is developed for supporting automated experimentation with many variables simultaneously, live in a production environment.The research in the thesis is conducted under the design science paradigm using empirical research methods, with simulation experiments of tool proposals and a multi-case study on company usage of CE. Other research methods include systematic literature review and theory building.From FACE we derive three factors that explain CE utility: (1) investments in data infrastructure, (2) user problem complexity, and (3) incentive structures for experimentation. Guidelines are provided on how to strive towards state-of-the-art CE based on company factors. All three factors are relevant for companies wanting to use CE, in particular, for those companies wanting to apply algorithms such as those in COMBO to support personalization of software to users' context in a process of continuous optimization

Unifying interaction across distributed controls in a smart environment using anthropology-based computing to make human-computer interaction "Calm"

Rather than adapt human behavior to suit a life surrounded by computerized systems, is it possible to adapt the systems to suit humans? Mark Weiser called for this fundamental change to the design and engineering of computer systems nearly twenty years ago. We believe it is possible and offer a series of related theoretical developments and practical experiments designed in an attempt to build a system that can meet his challenge without resorting to black box design principles or Wizard of Oz protocols. This culminated in a trial involving 32 participants, each of whom used two different multimodal interactive techniques, based on our novel interaction paradigm, to intuitively control nine distributed devices in a smart home setting. The theoretical work and practical developments have led to our proposal of seven contributions to the state of the art