The Case for Graph-Based Recommendations

Recommender systems have been intensively used to create personalised profiles, which enhance the user experience. In certain areas, such as e-learning, this approach is short-sighted, since each student masters each concept through different means. The progress from one concept to the next, or from one lesson to another, does not necessarily follow a fixed pattern. Given these settings, we can no longer use simple structures (vectors, strings, etc.) to represent each user's interactions with the system, because the sequence of events and their mapping to user's intentions, build up into more complex synergies. As a consequence, we propose a graph-based interpretation of the problem and identify the challenges behind (a) using graphs to model the users' journeys and hence as the input to the recommender system, and (b) producing recommendations in the form of graphs of actions to be taken

Personalized Course Sequence Recommendations

Given the variability in student learning it is becoming increasingly important to tailor courses as well as course sequences to student needs. This paper presents a systematic methodology for offering personalized course sequence recommendations to students. First, a forward-search backward-induction algorithm is developed that can optimally select course sequences to decrease the time required for a student to graduate. The algorithm accounts for prerequisite requirements (typically present in higher level education) and course availability. Second, using the tools of multi-armed bandits, an algorithm is developed that can optimally recommend a course sequence that both reduces the time to graduate while also increasing the overall GPA of the student. The algorithm dynamically learns how students with different contextual backgrounds perform for given course sequences and then recommends an optimal course sequence for new students. Using real-world student data from the UCLA Mechanical and Aerospace Engineering department, we illustrate how the proposed algorithms outperform other methods that do not include student contextual information when making course sequence recommendations

Improving package recommendations through query relaxation

Recommendation systems aim to identify items that are likely to be of interest to users. In many cases, users are interested in package recommendations as collections of items. For example, a dietitian may wish to derive a dietary plan as a collection of recipes that is nutritionally balanced, and a travel agent may want to produce a vacation package as a coordinated collection of travel and hotel reservations. Recent work has explored extending recommendation systems to support packages of items. These systems need to solve complex combinatorial problems, enforcing various properties and constraints defined on sets of items. Introducing constraints on packages makes recommendation queries harder to evaluate, but also harder to express: Queries that are under-specified produce too many answers, whereas queries that are over-specified frequently miss interesting solutions. In this paper, we study query relaxation techniques that target package recommendation systems. Our work offers three key insights: First, even when the original query result is not empty, relaxing constraints can produce preferable solutions. Second, a solution due to relaxation can only be preferred if it improves some property specified by the query. Third, relaxation should not treat all constraints as equals: some constraints are more important to the users than others. Our contributions are threefold: (a) we define the problem of deriving package recommendations through query relaxation, (b) we design and experimentally evaluate heuristics that relax query constraints to derive interesting packages, and (c) we present a crowd study that evaluates the sensitivity of real users to different kinds of constraints and demonstrates that query relaxation is a powerful tool in diversifying package recommendations

Evaluation of systems for piperun installation

The purpose of this thesis project was to analyse and evaluate different systems for installation of piperun in ceilings. Main focus lay on how the systems influence the installer from an ergonomic point of view as well as productivity in terms of installation time. Several studies have been performed during this project using a variety of methods such as observations, interviews, questionnaires and hierarchical task analysis. The results from these studies showed that often a variety of different systems for pipe installations are used. The working conditions of Plumbing and Heating (P&H) installers are very demanding and the categories working postures/loads/space, climate and lighting conditions are most unsatisfactory. Furthermore 60% of the participants of the questionnaire study had Musculoskeletal Disorders (MSD) and believed it to be work-related. These results led to the design of a test named Posture and Time study. During the Posture and Time study three systems for pipe installation were evaluated. System 1 represented a more traditional and inexpensive system, system 2 was intermediate and system 3 represented the most modern and expensive version. The systems were analysed using two methods for posture analysis, Hand Arm Risk assessment Method (HARM) and Rapid Entire Body Assessment (REBA). A time study was also conducted in order to define the installation times for both systems. The time study showed that systems 2 and 3 were about equally fast to install while system 1 took about 30 % longer. HARM and REBA both turned out to be to unspecific to define which system was better from an ergonomic aspect although they gave valuable insight to the importance of choosing good working postures. To get objective results of how the systems affect the P&H installer perhaps methods such as Electromyography or Inclinometry could be used. Even though the work posture analysis did not show a clear difference between the three systems, observations and interviews with P&H installers made it clear that they found system 1 to contain too many small parts and operations and therefore found this system harder to install. All studies performed during this thesis project have been very small in terms of number of participants. The project is therefore to be seen as a pilot study of how an investigation like this could be executed. Results from the studies have, when possible, been compared to statistic data in order to increase the reliability. However if more generalizing results are preferred, the studies need to be larger in scale

Formation of an Innovative Competitiveness Management System of the Enterprise: On the Case of Ukraine's Healthcare

In modern conditions, the priority task for Ukraine is the institutional transformation of the economy, the main purpose of which is to ensure sustainable socio-economic development. The article discusses the theoretical and methodological foundations and develops scientific and practical recommendations for the formation of an innovative competitiveness management system based on the example of healthcare institutions in Ukraine. Based on the analysis, the main characteristics of the innovative development of health care institutions are determined, interaction schemes for participants in the innovation process in the health care system and the mechanism for developing and implementing competitive strategies in the health care institution management system are developed. The calculation of the integral indicator of assessing the effectiveness of the use of labor resources as the most important component of assessing the level of competitiveness of an enterprise

eHealth technology in forensic mental healthcare:Recommendations for achieving benefits and overcoming barriers

While eHealth technologies such as web-based interventions, mobile apps, and virtual reality have the potential to be of added value for forensic mental healthcare, there is a gap between this potential and the current situation in practice. The goal of this study was to identify recommendations to bridge this gap. In total, 21 semi-structured interviews and 89 questionnaires were conducted in a Dutch forensic mental healthcare sample consisting of professionals, patients, and eHealth experts. Based on the broad range of identified recommendations, it can be concluded that attention should be paid to the characteristics of professionals, patients, technology, and the organization throughout the development, implementation and evaluation of eHealth

Item Retrieval as Utility Estimation

Retrieval systems have greatly improved over the last half century, estimating relevance to a latent user need in a wide variety of areas. One area that has not enjoyed such advancements is searching for items by attribute values, a common activity in e-commerce and science, particularly given numeric values. Existing item retrieval systems assume the user has a firm grasp of their own desires and can formulate a good Boolean or SQL-style query to retrieve items, as one would do with a database. A contrasting approach would be to estimate how well items match the user?s latent desires and return items ranked by this estimation. Towards this end, we present a retrieval model inspired by multi-criteria decision making theory, concentrating on numeric attributes. We evaluate our novel approach, the de-facto standard of Boolean retrieval, and several models proposed in the literature, in two user studies using Amazon Mechanical Turk. We use a competitive game to motivate test subjects and compare methods based on the results of the subjects? initial query and their success in the game. In our experiments, our new method signi cantly outperformed the others, whereas the Boolean approaches had the worst performance