Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback

In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the learning of a recommender with biased feedback is one of the most challenging problems, as it is widely known that naive approaches under selection bias often lead to suboptimal results. A well-established solution for the problem is using propensity scoring techniques. The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of its propensity. However, the performance of the propensity-based unbiased estimation approach is often affected by choice of the propensity estimation model or the high variance problem. To overcome these limitations, we propose a model-agnostic meta-learning method inspired by the asymmetric tri-training framework for unsupervised domain adaptation. The proposed method utilizes two predictors to generate data with reliable pseudo-ratings and another predictor to make the final predictions. In a theoretical analysis, a propensity-independent upper bound of the true performance metric is derived, and it is demonstrated that the proposed method can minimize this bound. We conduct comprehensive experiments using public real-world datasets. The results suggest that the previous propensity-based methods are largely affected by the choice of propensity models and the variance problem caused by the inverse propensity weighting. Moreover, we show that the proposed meta-learning method is robust to these issues and can facilitate in developing effective recommendations from biased explicit feedback.Comment: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20

Clustering and recommendation techniques for access control policy management

Managing access control policies can be a daunting process, given the frequent policy decisions that need to be made, and the potentially large number of policy rules involved. Policy management includes, but is not limited to: policy optimization, configuration, and analysis. Such tasks require a deep understanding of the policy and its building compo- nents, especially in scenarios where it frequently changes and needs to adapt to different environments. Assisting both administrators and users in performing these tasks is impor- tant in avoiding policy misconfigurations and ill-informed policy decisions. We investigate a number of clustering and recommendation techniques, and implement a set of tools that assist administrators and users in managing their policies. First, we propose and imple- ment an optimization technique, based on policy clustering and adaptable rule ranking, to achieve optimal request evaluation performance. Second, we implement a policy analysis framework that simplifies and visualizes analysis results, based on a hierarchical cluster- ing algorithm. The framework utilizes a similarity-based model that provides a basis of risk analysis on newly introduced policy rules. In addition to administrators, we focus on regular individuals whom nowadays manage their own access control polices on a regular basis. Users are making frequent policy decisions, especially with the increasing popular- ity of social network sites, such as Facebook and Twitter. For example, users are required to allow/deny access to their private data on social sites each time they install a 3rd party application. To make matters worse, 3rd party access requests are mostly uncustomizable by the user. We propose a framework that allows users to customize their policy decisions on social sites, and provides a set of recommendations that assist users in making well- informed decisions. Finally, as the browser has become the main medium for the users online presence, we investigate the access control models for 3rd party browser extensions. Even though, extensions enrich the browsing experience of users, they could potentially represent a threat to their privacy. We propose and implement a framework that 1) monitors 3rd party extension accesses, 2) provides fine-grained permission controls, and 3) Provides detailed permission information to users in effort to increase their privacy aware- ness. To evaluate the framework we conducted a within-subjects user study and found the framework to effectively increase user awareness of requested permissions

Politische Maschinen: Maschinelles Lernen für das Verständnis von sozialen Maschinen

This thesis investigates human-algorithm interactions in sociotechnological ecosystems. Specifically, it applies machine learning and statistical methods to uncover political dimensions of algorithmic influence in social media platforms and automated decision making systems. Based on the results, the study discusses the legal, political and ethical consequences of algorithmic implementations.Diese Arbeit untersucht Mensch-Algorithmen-Interaktionen in sozio-technologischen Ökosystemen. Sie wendet maschinelles Lernen und statistische Methoden an, um politische Dimensionen des algorithmischen Einflusses auf Socialen Medien und automatisierten Entscheidungssystemen aufzudecken. Aufgrund der Ergebnisse diskutiert die Studie die rechtlichen, politischen und ethischen Konsequenzen von algorithmischen Anwendungen

What is the Relationship Between Quality of Life and Coping Strategies of Adults with Celiac Disease Adhering to a Gluten Free Diet?

Until recently, celiac disease was thought to be rare in the United States. However over the past ten years, the reported prevalence has increased from 1 in 4600 persons to 1 in 133 persons. The latest estimate makes the prevalence comparable to the prevalence in Europe, where the disease is considered to be common. Celiac disease is a chronic illness occurring in genetically susceptible persons resulting in inflammatory changes in the upper small bowel as a consequence of intolerance to the gliadin in ingested wheat, rye, and barley. Fortunately, celiac disease can be effectively managed by strict adherence to a gluten free diet. However, dietary management can be quite challenging. The present descriptive, correlational research study included 156 adults self-reporting a diagnosis of celiac disease. The purpose of this study was to examine factors and perceived causes that interfere with adherence to a gluten free diet, to identify coping strategies, and to examine the relationship between coping strategies and quality of life. The theoretical framework was a combination of two theoretical models: 1) Lazarus model of stress and 2) the model of behavioral self-regulation by Carver and Sheier. Instruments used were the Demographic Information and Health and Diet History Questionnaire, the Psychological General Well-Being Index, and the Brief COPE. Results from the study indicated that problems outside the home, especially in restaurants and the expense of gluten free foods are factors that interfere with dietary adherence. A moderate negative relationship was found between quality of life and stress with 54 percent of participants reporting a minimal amount of stress. Emotion focused coping was found to have a negative effect on quality of life. Recommendations based on research findings suggest further investigation of the negative relationship between quality of life and stress with a more controlled sample. Nurses can also investigate the use of cognitive-behavioral interventions to decrease the negative effects of emotion focused coping

Where to place which sensor to measure sedentary behaviour? A method development and comparison among various sensor placements and signal types

Background: Sedentary Behaviour (SB) is associated with several chronic diseases and especially office workers are at increased risk. SB is defined by a sitting or reclined body posture with an energy expenditure ≤1.5 METs. However, current objective methods to measure SB are not consistent with its definition. There is no consensus on which sensor placement and type to be used. Aim: To compare the accuracy of newly developed artificial intelligence models for 15 sensor placements in combination with four signal types (accelerometer only/plus gyroscope and/or magnetometer) to detect posture and physical in-/activity while desk-based activities. Method: Signal features for the model development were extracted from sensor raw data of 30 office workers performing 10 desk-based tasks, each lasting 5 minutes. Direct observation (posture) and indirect calorimetry (in-/activity) served as reference criteria. The best classification model for each sensor was identified and compared among the sensor placements, both using Friedman and post-hoc Wilcoxon tests (p≤0.05). Results: Posture was most accurately measured with a lower body sensor, while in-/activity was most accurately measured with an upper body or waist sensor. The inclusion of additional signal types improved the posture classification for some placements, while the acceleration signal already contained the relevant signal information for the in-/activity classification. Overall, the thigh accelerometer most accurately classified desk-based SB. Conclusion: This study favours, in line with previous work, the measurement of SB with a thigh worn accelerometer, and adds the information that this sensor is also accurate in measuring physical in-/activity while sitting and standing.Swiss National Science FoundationAccepte

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Effects of word-of-mouth communication on purchasing decisions in restaurants: A path analytic study

This study investigated the restaurant word-of-mouth communication structure. Main constructs of the word-of-mouth process on purchasing decisions were identified and their relationships were examined in a restaurant setting. Consequently, a restaurant word-of-mouth model was proposed; The main interests of study are as follows: first, to identify the main factors of restaurant word-of-mouth communication; second, to discover which word-of-mouth factors directly affect the consumer\u27s restaurant product/service purchase decision; and third, to find out the degree to which word-of-mouth factors determine the consumer\u27s word-of-mouth search efforts for a restaurant. The study also looked at the mediating effect of word-of-mouth search efforts on the purchase decision. In the end, the proposed word-of-mouth model was compared to a general-services word-of-mouth model to determine which model better explains the restaurant word-of-mouth communication structure; As the result of an extensive literature review, eight restaurant word-of-mouth constructs and fourteen hypotheses were formulated. They were based on the theoretical background of communication models, on Bansal and Voyer\u27s word-of-mouth model, and on the Theory of Planned Behavior. The data were collected via a web-based survey. The Structural Equation Modeling method was adopted to test hypotheses and eventually to answer research questions. The findings of this study suggest that factors of word-of-mouth sender\u27s expertise, reference group, and word-of-mouth search effort influence the consumer\u27s restaurant service/product purchase decision. For example, if the sender seems experienced, and if the receiver cares about how others see him when he makes an additional word-of-mouth search effort, then the influence of the sender\u27s word-of-mouth on the receiver\u27s purchase decision increases. Similarly, the perceived word-of-mouth receiver\u27s expertise, perceived risk, and self-restaurant image congruence constructs turned out to be influential factors for the consumer\u27s word-of-mouth search effort. It seems that the more educated (experienced) customers actively search word-of-mouth information when they feel more risk about the restaurant choice and when they see more of image congruence between the restaurant and themselves; It was interesting that most of the experiences reported in this study involved positive word-of-mouth. It seems that positive word-of-mouth has a bigger impact on a restaurant consumer\u27s word-of-mouth experience. It is also noteworthy that the word-of-mouth channel most respondents used was face-to-face