Browsing by Subject "Personalized"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A Personalized Recommender System with Correlation Estimation
    (2018-05) Yang, Fan
    Recommender systems aim to predict users’ ratings on items and suggest certain items to users that they are most likely to be interested in. Recent years there has been a lot of interest in developing recommender systems, especially personalized recommender systems to efficiently provide personalized services and increase conversion rates in commerce. Personalized recommender systems identify every individual’s preferences through analyzing users’ behavior, and sometimes also analyzing user and item feature information. Existing recommender system methods typically ignore the correlations between ratings given by a user. However, based on our observation the correlations can be strong. We propose a new personalized recommender system method that takes into account the correlation structure of ratings by a user. General precision matrices are estimated for the ratings of each user and clustered among users by supervised clustering. Moreover, in the proposed model we utilize user and item feature information, such as the demographic information of users and genres of movies. Individual preferences are estimated and grouped over users and items to find similar individuals that are close in nature. Computationally, we designed an algorithm applying the difference of convex method and the alternating direction method of multipliers to deal with the nonconvexity of the loss function and the fusion type penalty respectively. Theoretical rate of convergence is investigated for our new method. We also show theoretically that incorporating the correlation structure gives higher asymptotic efficiency of the estimators compared to ignoring it. Both simulation studies and Movielens data indicate that our method outperforms existing competitive recommender system methods.
  • Thumbnail Image
    Item
    Personalized surgical risk assessment using population-based data analysis
    (2013-02) AbuSalah, Ahmad Mohammad
    The volume of information generated by healthcare providers is growing at a relatively high speed. This tremendous growth has created a gap between knowledge and clinical practice that experts say could be narrowed with the proper use of healthcare data to guide clinical decisions and tools that support rapid information availability at the clinical setting. In this thesis, we utilized population surgical procedure data from the Nationwide Inpatient Sample database, a nationally representative surgical outcome database, to answer the question of how can we use population data to guide the personalized surgical risk assessment process. Specifically, we provided a risk model development approach to construct a model-driven clinical decision support system utilizing outcome predictive modeling techniques and applied the approach on a spinal fusion surgery which was selected as a use case. We have also created The Procedure Outcome Evaluation Tool (POET); which is a data-driven system that provides clinicians with a method to access NIS population data and submit ad hoc multi-attribute queries to generate average and personalized data-driven surgical risks. Both systems use patient demographics and comorbidities, hospital characteristics, and admission information data elements provided by NIS data to inform clinicians about inpatient mortality, length of stay, and discharge disposition status.