Learning from Sets of Items in Recommender Systems
E-Learning systems can support real-time monitoring of learners? learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users? motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students? motivation. Existing research has rarely attempted to model dyslexic users? motivation in e-learning context from a comprehensive perspective. This paper has conceived a hybrid approach, namely combining the strengths of qualitative and quantitative analysis methods, to motivation modeling. It examines a variety of factors which affect dyslexic students? motivation to engage in e-learning systems from psychological, behavioral and technical perspectives, and establishes their interrelationships. Specifically, the study collects data from a multi-item Likert-style questionnaire to measure relevant factors for conceptual motivation modelling. It then applies the Structural Equation Modeling approach to determine the quantitative mapping between dyslexic students? continued use intention and motivational factors, followed by discussions about theoretical findings and design instructions according to our motivation model. Our research has led to a novel motivation model with new constructs of Learning Experience, Reading Experience, Perceived Control and Perceived Privacy. Initial results have indicated direct effects of Attitudes Toward School, Visual Attractiveness, Reading Experience and Utilization on continued use intention.
Eating activity monitoring through wearable sensors can potentially enable interventions based on eating speed to mitigate the risks of critical healthcare problems such as obesity or diabetes. Eating actions are poly-componential gestures composed of sequential arrangements of three distinct components interspersed with gestures that may be unrelated to eating. This makes it extremely challenging to accurately identify eating actions. The primary reason for the lack of acceptance of state-of-art eating action monitoring techniques include: i) the need to install wearable sensors that are cumbersome to wear or limit mobility of the user, ii) the need for manual input from the user, and iii) poor accuracy if adequate manual input is not available. In this work, we propose a novel methodology, IDEA that performs accurate eating action identification in eating episodes with an average F1-score of 0.92. IDEA uses only a single wrist-worn sensor and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user. %It can also be used to automatically annotate other poly-componential gestures.
Towards User-Adaptive Visualizations: Comparing and Combining Eye-Tracking and Interaction Data for the Real-Time Prediction of User Cognitive Abilities
Lifestyle interventions that focus on diet are crucial in self-management and prevention of many chronic conditions such as obesity, cardiovascular disease, diabetes, and cancer. Such interventions require a diet monitoring approach to estimate overall dietary composition and energy intake. Although wearable sensors have been used to estimate eating context (e.g., food type and eating time), accurate monitoring of diet intake has remained a challenging problem. In particular, because monitoring diet intake is a self-administered task that involves the end-user to record or report on their nutrition intake, current diet monitoring technologies are prone to measurement errors related to challenges of human memory, estimation, and bias. New approaches based on mobile devices have been proposed to facilitate the process of diet intake recording. These technologies require individuals to use mobile devices such as smartphones to record nutrition intake by either entering text or taking images of the food. Such approaches, however, suffer from errors due to low adherence to technology adoption and time sensitivity to the dietary intake context. In this article, we introduce EZNutriPal1, an interactive diet monitoring system that operates on unstructured mobile data such as speech and free-text to facilitate dietary recording, real-time prompting, and personalized nutrition monitoring. EZNutriPal features a Natural Language Processing (NLP) unit that learns incrementally to add user-specific nutrition data and rules to the system. To prevent missing data that are required for dietary monitoring (e.g., calorie intake estimation), EZNutriPal devises an interactive operating mode that prompts the end-user to complete missing data in real-time. Additionally, we propose a combinatorial optimization approach to identify most appropriate pairs of food name and portion size in complex input sentences. We evaluate the proposed approach using real data collected with 23 subjects who participated in two user studies conducted in 13 days each. The results demonstrate that EZNutriPal achieves 89.7% in calorie intake estimation. We also assess the impacts of the incremental training and interactive prompting on the accuracy of calorie intake estimation and show that incremental training and interactive prompting improve the accuracy performance of computing dietary monitoring by 49.6% and 29.1%, respectively, compare to a system without such computing units.
In this paper, we propose novel techniques to predict a user?s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one?s decision or choice in real life. In this work, we integrate user interactions in Twitter and IMDb to derive interesting relations between human psychological attributes and their movie preferences. In particular, we first predict a user?s movie genre preferences from the personality and value scores of the user derived from her tweets. Second, we also develop models to predict user movie rating behavior from her tweets in Twitter and movie genre and storyline preferences from IMDb. We further strengthen the movie rating model by incorporating the user reviews. In the above models, we investigate the role of personality and values independently and combinedly while predicting movie genre preferences and movie rating behaviors. We find that our combined models significantly improve the accuracy than that of a single model that is built by using personality or values independently. We also compare our technique with the traditional movie genre and rating prediction techniques. The experimental results show that our models are effective in recommending movies to users.
The primary well-controlled study of 900 participants found that personal presentation choices in professional emails (non-content changes such as Profile Avatar & Signature) impact the recipient?s perception of the sender?s personality, and the quality of the email itself. By understanding the role these choices play, employees can gain better control over how they influence the recipient of their messages. Results further indicated that while some variations can positively impact the recipient?s view of the sender, these same variations often also have negative side-efects. This implies that many seemingly innocuous presentation decisions should be made in the context of who is receiving the email, and if these effects negatively impact the content of the message. For example, while statements in a Signature about the email having being written on a phone are included to preemptively apologize for typing mistakes, this causes the sender to appear less agreeable, less conscientious, and less open, and the email itself appears less well written and more poorly formatted even though the email itself did not change in the study.
The explanation interface has been recognized important in recommender systems because it can allow users to better judge the relevance of recommendations to their preference and hence make more informed decisions. In different product domains, the specific purpose of explanation can be different. For high-investment products (e.g., digital cameras, laptops), how to educate the typical type of new buyers about product knowledge and consequently improve their preference certainty and decision quality is essentially crucial. With this objective, we have developed a novel tradeoff-oriented explanation interface that particularly takes into account sentiment features as extracted from product reviews to generate recommendations and explanations in a category structure. In this manuscript, we report two user studies conducted on this interface. The first is an online user study (in both before-after and within-subjects setups) that compared our prototype system with the traditional one that purely considers static specifications for explanation. The experimental results reveal that adding sentiment-based explanations can help increase users' product knowledge, preference certainty, perceived information usefulness, perceived recommendation transparency and quality, and purchase intention. Inspired by those findings, we performed a follow-up eye-tracking lab experiment in order to in-depth investigate how users view information on the interface. This study shows integrating sentiment features with static specifications in the tradeoff-oriented explanations prompted users to not only view more recommendations from various categories, but also stay longer on reading explanations. The results also infer users' inherent information needs for sentiment features during product evaluation and decision making. At the end, we discuss the work's practical implications from three major aspects, i.e., new users, category interface, and explanation purpose.
A proposal for a unified theory of learned trust implemented in a cognitive architecture is presented. A published computational cognitive model of learned trust is critically reviewed. A revised model is proposed to overcome the limitations of the published model and expand its scope of applicability. The revised model integrates several seemingly unrelated categories of findings from the literature on interpersonal and human-machine interactions and makes unintuitive predictions for future studies. The implications of the model for the advancement of the theory on trust are discussed.