Content-boosted Matrix Factorization Techniques for Recommender Systems

Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable

Topology Control for Maintaining Network Connectivity and Maximizing Network Capacity Under the Physical Model

In this paper we study the issue of topology control under the physical Signal-to-Interference-Noise-Ratio (SINR) model, with the objective of maximizing network capacity. We show that existing graph-model-based topology control captures interference inadequately under the physical SINR model, and as a result, the interference in the topology thus induced is high and the network capacity attained is low. Towards bridging this gap, we propose a centralized approach, called Spatial Reuse Maximizer (MaxSR), that combines a power control algorithm T4P with a topology control algorithm P4T. T4P optimizes the assignment of transmit power given a fixed topology, where by optimality we mean that the transmit power is so assigned that it minimizes the average interference degree (defined as the number of interferencing nodes that may interfere with the on-going transmission on a link) in the topology. P4T, on the other hand, constructs, based on the power assignment made in T4P, a new topology by deriving a spanning tree that gives the minimal interference degree. By alternately invoking the two algorithms, the power assignment quickly converges to an operational point that maximizes the network capacity. We formally prove the convergence of MaxSR. We also show via simulation that the topology induced by MaxSR outperforms that derived from existing topology control algorithms by 50%-110% in terms of maximizing the network capacity

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version

Effectiveness of a Multimodal Mindfulness Program for Student Health Care Professionals: A Randomized Controlled Trial

Background: The effectiveness of a multimodal mindfulness program incorporating traditional and nontraditional forms of active and nonactive meditation practices with a sample of occupational and physical therapy students was assessed in this study. Method: Thirty-six participants were randomly assigned to an intervention or control group. The 8-week mindfulness program consisted of one weekly 40-min in-person group session and four weekly 10-min online guided meditations. Pre and postintervention measures included the Perceived Stress Scale (PSS), Student Stress Management Scale (SSMS), mindfulness activity log, open-ended qualitative questionnaire, GPA, and counseling visit frequency. Results: Statistically significant differences, with large effect sizes, were found between intervention and control group PSS (Z=-4.291, pd=-1.84) and SSMS (Z=-3.330, pd=-1.27) postintervention scores. Statistically significant differences, with large effect sizes, were found between intervention group pre and postmindfulness activity ratings for each week and all weeks combined (Z=-12.599, pd=1.29). Qualitative data revealed eight themes including greater sleep quality, energy levels, self-compassion, and life-work balance. No statistically significant differences were found between intervention and control group counseling visit frequency and GPA. Conclusion: As this is preliminary data about a novel intervention with a small student sample, effectiveness of this intervention should be further explored in a replication study

Navigation under Obstacle Motion Uncertainty using Markov Decision Processes

In terms of navigation, a central problem in the field of autonomous robotics is obstacle avoidance. This research explores how to navigate as well as avoid obstacles by leveraging what is known of the environment to determine decisions with new incoming information during execution. The algorithm presented in this work is divided into two procedures: an offline process that uses prior knowledge to navigate toward the goal; and an online execution strategy that leverages results obtained offline to drive safely towards the target when new information is encountered (e.g., obstacles). To take advantage of what is known offline, the navigation problem was formulated as a Markov Decision Process (MDP) where the environment is characterized as an occupancy grid. Baseline dynamic programming techniques were used to solve this, producing general behaviors that drive the robot (or agent) toward the goal and a value function which encodes the value of being in particular states. Then during online execution, the agent uses these offline results and surrounding local information of the environment to operate (e.g., data from a LIDAR sensor). This locally acquired information, which may contain new data not seen prior, is represented as a small occupancy grid and leverages the offline obtained value function to define local goals allowing the agent to make short term plans. When the agent encounters an obstacle locally, the problem becomes a Partially Observable Markov Decision Process (POMDP) since it is uncertain where these obstacles will be in the next state. This is solved by utilizing an approximate planner (QMDP) that uses uncertainty of the obstacle motion and considers all possible obstacle state combinations in the next time step to determine the best action. The approximate planner can quickly solve the POMDP, due to the small size of the local occupancy grid and by using the behaviors produced offline to help speed up convergence, which opens the possibility for this procedure to be executed in real time, on a physical robot. Two simulated environments were created, varying in complexity and dynamic obstacles. Simulation results under complex conditions with narrow operable spaces and many dynamic obstacles show the proposed algorithm has approximately an 85% success rate, in test cases with cluttered environments and multiple dynamic obstacles, and is shown to produce safer trajectories than the baseline approach, which had roughly a 37% success rate, under the assumptions that dynamic obstacles can only move a short distance by the next time step

Microstructural Evolution in Friction Stir Welding of Ti-5111

Titanium and titanium alloys have shown excellent mechanical, physical, and corrosion properties. To address the needs of future naval combatants, this research examines an alternative joining technology, friction stir welding (FSW). Friction stir welding uses a non-consumable tool to generate frictional heat to plastically deform and mix metal to form a consolidated joint. This work focuses on FSW of Ti-5111 (Ti-5Al-1Sn-1Zr-1V-0.8Mo), a near alpha alloy. This study aims to gain a fundamental understanding of the relationship between processing parameters, microstructure, and mechanical properties of experimental 12.7mm and 6.35mm Ti-5111 friction stir welds. The resulting weld microstructure shows significant grain refinement within the weld compared to the base metal. The weld microstructures show a fully lamellar colony structure with peak welding temperatures exceeding beta transformation temperature. The friction stir weld shows material texture strengthening of the BCC F fiber component before transformation to D2 shear texture in the stir zone. Transmission electron microscopy results of the base metal and the stir zone show a lath colony-type structure with low dislocation density and no lath grain substructure. In situ TEM heating experiments of Ti-5111 friction stir welded material show transformation to the high temperature beta phase at significantly lower temperatures compared to the base metal. Thermal and deformation mechanisms within Ti-5111 were examined through the use of thermomechanical simulation. Isothermal constant strain rate tests show evidence of dynamic recrystallization and deformation above beta transus when compared with the FSW thermal profile without deformation. Subtransus deformation shows kinking and bending of the existing colony structure without recrystallization. Applying the friction stir thermal profile to constant strain rate deformation successfully reproduced the friction stir microstructure at a peak temperature of 1000ºC and a strain rate of 10/s. These results provide unique insight into the strain, strain rates, and temperatures regime within the process. Finally, the experimental thermal and deformation fields were compared using ISAIAH, a Eulerian based three-dimensional model of friction stir welding. These results are preliminary but show promise for the ability of the model to compute thermal fields for material flow, model damage prediction, and decouple texture evolution for specific thermomechanical histories in the friction stir process