3,562 research outputs found

    Customer purchase behavior prediction in E-commerce: a conceptual framework and research agenda

    Get PDF
    Digital retailers are experiencing an increasing number of transactions coming from their consumers online, a consequence of the convenience in buying goods via E-commerce platforms. Such interactions compose complex behavioral patterns which can be analyzed through predictive analytics to enable businesses to understand consumer needs. In this abundance of big data and possible tools to analyze them, a systematic review of the literature is missing. Therefore, this paper presents a systematic literature review of recent research dealing with customer purchase prediction in the E-commerce context. The main contributions are a novel analytical framework and a research agenda in the field. The framework reveals three main tasks in this review, namely, the prediction of customer intents, buying sessions, and purchase decisions. Those are followed by their employed predictive methodologies and are analyzed from three perspectives. Finally, the research agenda provides major existing issues for further research in the field of purchase behavior prediction online

    Machine Learning for Physiological Time Series: Representing and Controlling Blood Glucose for Diabetes Management

    Full text link
    Type 1 diabetes is a chronic health condition affecting over one million patients in the US, where blood glucose (sugar) levels are not well regulated by the body. Researchers have sought to use physiological data (e.g., blood glucose measurements) collected from wearable devices to manage this disease, either by forecasting future blood glucose levels for predictive alarms, or by automating insulin delivery for blood glucose management. However, the application of machine learning (ML) to these data is hampered by latent context, limited supervision and complex temporal dependencies. To address these challenges, we develop and evaluate novel ML approaches in the context of i) representing physiological time series, particularly for forecasting blood glucose values and ii) decision making for when and how much insulin to deliver. When learning representations, we leverage the structure of the physiological sequence as an implicit information stream. In particular, we a) incorporate latent context when predicting adverse events by jointly modeling patterns in the data and the context those patterns occurred under, b) propose novel types of self-supervision to handle limited data and c) propose deep models that predict functions underlying trajectories to encode temporal dependencies. In the context of decision making, we use reinforcement learning (RL) for blood glucose management. Through the use of an FDA-approved simulator of the glucoregulatory system, we achieve strong performance using deep RL with and without human intervention. However, the success of RL typically depends on realistic simulators or experimental real-world deployment, neither of which are currently practical for problems in health. Thus, we propose techniques for leveraging imperfect simulators and observational data. Beyond diabetes, representing and managing physiological signals is an important problem. By adapting techniques to better leverage the structure inherent in the data we can help overcome these challenges.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163134/1/ifox_1.pd

    Motor Imagery to Facilitate Sensorimotor Re-Learning (MOTIFS): Integrating Dynamic Motor Imagery in Current Treatment of Knee Injury

    Get PDF
    Traumatic knee injury is common in physical activity that includes jumping and cutting movements, and most commonly include anterior cruciate ligament (ACL) or meniscus injuries. Surgical or non-surgical intervention strategies may be chosen, but treatment will include a physical-therapist led physical training program. The aim of this training is to strengthen and stabilize the knee. Despite receiving best-practice treatment, many are unable to return to their pre-injury activity level. Recent research has suggested that this may be explained, in part, by psychological factors such as fear of re-injury or lack of confidence. In addition to physical treatment, guidelines include recommendations to address psychological factors. The detail of how this can be done is lacking, and the extent to which psychological variables are adequately addressed is questionable. In response to this gap, we have developed the novel Motor Imagery to Facilitate Sensorimotor Re-Learning (MOTIFS) model, which integrates psychological training into physical rehabilitation protocols using a dynamic motor imagery intervention. MOTIFS increases realism and relevance while simultaneously physically and psychologically simulating activity-specific and individualized rehabilitation exercises. The aim of this thesis is therefore to develop and explore the efficacy of the MOTIFS model in physically and psychologically preparing knee-injured people for return to activity compared to care-as-usual rehabilitation. The primary hypothesis of this thesis is that the MOTIFS model will provide greater effects on patient-relevant outcomes and muscle function than current programs. In a first step, the effect of MOTIFS model on enjoyment and other self-reported outcomes was evaluated in a cross-over study (Paper I) in which uninjured people underwent training according to both MOTIFS and care-as-usual training protocols. Next, a protocol detailing an ongoing randomized controlled trial (Paper II) which will compare 12 weeks of MOTIFS and care-as-usual training in terms of psychological readiness to return to activity and functional performance. Finally, two interview studies were conducted in which physical therapists (Paper III) and Patients (Paper IV) in both MOTIFS and care-as-usual groups were interviewed about the experiences of rehabilitation training following traumatic knee injury.Results of this thesis show that the MOTIFS model has the potential to increase enjoyment of knee injury prevention and treatment exercises. Other self-reported outcomes were also improved, and the MOTIFS model does not seem to sacrifice movement quality, indicating that it is possible to modify exercises by integrating a dynamic motor imagery intervention. Results of the interview study with physical therapists indicates that those in the MOTIFS group perceive a greater focus on psychological factors while using the new training model, and believe that it is an effective method of increasing patient readiness to return to activity. Those in the care-as-usual group described their perception of rehabilitation training as having a mainly physical focus. They expressed a desire for more tools to address psychological factors, as they perceived patient reactions to be psychological in nature and felt they were ill equipped to handle these factors. Patients in the MOTIFS group perceived MOTIFS to be meaningful and a positive method of increasing their readiness to return to sport, owing to early exposure to activity, which helps them to feel that they have longer to prepare for their return. Those in the care-as-usual group perceive a lack of psychological focus, and their success was measured in terms of their physical progress through rehabilitation. Results indicate that the MOTIFS model may be a feasible and clinically implementable method of addressing psychological factors in rehabilitation training. As the randomized controlled trial is still ongoing, no conclusions can be drawn regarding the efficacy of the intervention on rehabilitation outcomes. However, given the results of Papers I, III and IV, it seems a promising start to bridge the gap between physical and psychological rehabilitation outcomes

    Proceedings of Mathsport international 2017 conference

    Get PDF
    Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

    Multiplexed simultaneous representations of cognitive and motor features, in the mouse medial prefrontal cortex, during a memory guided behavior

    Get PDF
    "When using behavioral paradigms to investigate the neural basis of certain behaviors, or cognitive processes, one must first make sure to completely understand how the subjects are solving them. Delayed response tasks have been successfully used in investigating WM at the behavioral and neural level, but, given their design, with the cue immediately giving away the future response, subjects have been found to use behavioral strategies to avoid the need of keeping a memory during their cue absent period. Here we present an head-fixed delayed response task on a treadmill, for mice, that allows us to precisely monitor the behavior of the animals while simultaneously performing multi-electrode acute recordings.(...)

    Sustainable consumption: towards action and impact. : International scientific conference November 6th-8th 2011, Hamburg - European Green Capital 2011, Germany: abstract volume

    Get PDF
    This volume contains the abstracts of all oral and poster presentations of the international scientific conference „Sustainable Consumption – Towards Action and Impact“ held in Hamburg (Germany) on November 6th-8th 2011. This unique conference aims to promote a comprehensive academic discourse on issues concerning sustainable consumption and brings together scholars from a wide range of academic disciplines. In modern societies, private consumption is a multifaceted and ambivalent phenomenon: it is a ubiquitous social practice and an economic driving force, yet at the same time, its consequences are in conflict with important social and environmental sustainability goals. Finding paths towards “sustainable consumption” has therefore become a major political issue. In order to properly understand the challenge of “sustainable consumption”, identify unsustainable patterns of consumption and bring forward the necessary innovations, a collaborative effort of researchers from different disciplines is needed

    Dynamic reorganization of the cortico-basal ganglia-thalamo-cortical network during task learning

    Full text link
    Adaptive behavior is coordinated by neuronal networks that are distributed across multiple brain regions such as in the cortico-basal ganglia-thalamo-cortical (CBGTC) network. Here, we ask how cross-regional interactions within such mesoscale circuits reorganize when an animal learns a new task. We apply multi-fiber photometry to chronically record simultaneous activity in 12 or 48 brain regions of mice trained in a tactile discrimination task. With improving task performance, most regions shift their peak activity from the time of reward-related action to the reward-predicting stimulus. By estimating cross-regional interactions using transfer entropy, we reveal that functional networks encompassing basal ganglia, thalamus, neocortex, and hippocampus grow and stabilize upon learning, especially at stimulus presentation time. The internal globus pallidus, ventromedial thalamus, and several regions in the frontal cortex emerge as salient hub regions. Our results highlight the learning-related dynamic reorganization that brain networks undergo when task-appropriate mesoscale network dynamics are established for goal-oriented behavior
    corecore