Mohammad Nawaz, Luvai Motiwalla, Amit V. Deokar
Mobile banking (MB) services offered by financial institutions are moving from being a strategic advantage to sustain competition fueled by the ubiquity of smartphones among banking customers. Yet, MB apps are difficult to use with small screens and new features being added constantly. While personalization features in the form of recommender systems are commonplace in ecommerce applications, personalization in MB is in early stages. This study focuses on adapting MB application interface by using a data analytic technique; the novelty and contribution of our MB application is its focus on real-time analysis of user’s prior interactions with the system and adapts to their needs with real-time analysis of user’s prior interactions with the system to improve user experience and usage. PERMA system is a next generation of Adaptive Hypermedia System (AHS) where our MB app provides personalized content, presentation, and navigational support
Michael Bub, Evgeny Volynsky, Wolfgang Wörndl
Smartwatches are becoming more and more popular and offer an alternative to smartphones for frequent but short interactions. A promising use case for smartwatches is to proactively notify users about recommended items in their vicinity. In this work, we have investigated the usage of smartwatches for displaying proactive recommendations and offering quick feedback options. In order to assess the user acceptance on smartphone, smartwatch and a combination of both devices in a study, we developed a corresponding smartphone and smartwatch application for restaurant recommendations. The results from the study showed that participants preferred notifications on the smartwatch with gesture-based feedback over other options.
Maciej Jan Korzepa, Benjamin Johansen, Michael Kai Petersen, Jan Larsen, Jakob Eg Larsen, Niels Henrik Pontoppidan
Despite the technological advancement of modern hearing aids, many users leave their devices unused due to little perceived benefit. This problem arises from the limitations of the current fitting procedure that rarely takes into account 1) the perceptual differences between users not explained by measurable hearing loss characteristics and 2) the variation in context-specific preferences within individuals. However, the recent emergence of smartphone-connected hearings aids opens the door to a new level of context awareness that can facilitate dynamic adaptation of settings to users’ changing needs. In this position paper, we discuss how user auditory intents could be modeled as context collected via mobile devices and suggest what kinds of contextual information are relevant when learning situation-specific intents and the corresponding preferences of hearing impaired users. Finally, we illustrate our ideas with several examples of real-life situations experienced by subjects from our study.
Yunlong Wang, Corinna Breitinger, Björn Sommer, Falk Schreiber, Harald Reiterer
In the domain of human behavior prediction, next-place prediction is an active research field. While prior work has applied sequential and temporal patterns for next-place prediction, no work has yet studied the prediction performance of combining sequential with temporal patterns compared to using them separately. In this paper, we address next-place prediction using the sequential and temporal patterns embedded in human mobility data that has been collected using the GPS sensor of smartphones. We test five next-place prediction methods, including single pattern-based methods and hybrid methods that combine temporal and sequential patterns. Instead of only examining average accuracy as in related work, we additionally evaluate the selected methods using incremental-prediction accuracy on two publicly available datasets (the MDC dataset and the StudentLife dataset). Our results suggest that (1) integrating multiple patterns is not necessarily more effective than using single patterns in average prediction accuracy, (2) most of the tested methods can outperform others for a certain time period (either for the prediction of all places or each place individually), and (3) average prediction accuracies of the top-three candidates using sequential patterns are relatively high (up to 0.77 and 0.91 in the median for both datasets). For real-time applications, we recommend applying multiple methods in parallel and choosing the prediction of the best method according to incremental-prediction accuracy. Lastly, we present an expert tool for visualizing the prediction results.
Exploring the Impact of Elaborateness and Indirectness on User Satisfaction in a Spoken Dialogue System
Juliana Miehle, Wolfgang Minker, Stefan Ultes
We present a study addressing the questions of how varying communication styles of a spoken user interface are perceived by users and whether there exist global preferences in the communication styles elaborateness and indirectness. A total of 60 participants had two conversations each with Amazon’s Alexa where Alexa used varying wordings for its output. In a post-survey, the participants had to rate statements to subjectively assess each dialogue as well as indicate which dialogue they preferred. The results show that the system’s communication style has a direct influence on the user’s satisfaction level as well as the user’s perception of the dialogue and imply that the preference in the system’s communication style is individual for every person. This emphasises the need for adaptive user interfaces.
Chiara Alzetta, Giovanni Adorni, Ilknur Celik, Frosina Koceva, Ilaria Torre
This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner’s question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user’s question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system.
Aravind Sesagiri Raamkumar, Schubert Foo
Recommendation techniques in scientific paper recommender systems (SPRS) have been generally evaluated in an offline setting, without much user involvement. Nonetheless, user relevance of recommended papers is equally important as system relevance. In this paper, we present a scientific paper recommender system (SPRS) prototype which was subject to both offline and user evaluations. The lessons learnt from the evaluation studies are described. In addition, the challenges and open questions for multi-method evaluation in SPRS are presented.
Zainab Zolaktaf, Omar AlOmeir, Rachel Pottinger
In this paper, we propose to evaluate recommender systems by conducting both offline and user-centric evaluations, while considering multiple quality aspects in realistic settings. This comprehensive evaluation would provide insight on how to improve the algorithms, and how to design better evaluation metrics, particularly for offline settings where it is cheaper to conduct evaluations. We present the preliminary offline evaluation results of several algorithms, using accuracy, novelty, and coverage metrics while considering the impact of dataset density. We propose to complement this offline evaluation with a user-centric evaluation that measures the users’ perceived quality of the same algorithms.