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Jiang, Zhen
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Machine Learning with Decision Trees and Multi-Armed Bandits: An Interactive Vehicle Recommender System

Carnegie Mellon University-Tong Yu, Ole Mengshoel
Ford Motor Co., Ltd.-Dominique Meroux, Zhen Jiang
Published 2019-04-02 by SAE International in United States
Recommender systems guide a user to useful objects in a large space of possible options in a personalized way. In this paper, we study recommender systems for vehicles. Compared to previous research on recommender systems in other domains (e.g., movies or music), there are two major challenges associated with recommending vehicles. First, typical customers purchase fewer cars than movies or pieces of music. Thus, it is difficult to obtain rich information about a customer’s vehicle purchase history. Second, content information obtained about a customer (e.g., demographics, vehicle preferences, etc.) is also difficult to acquire during a relatively short stay in a dealership. To address these two challenges, we propose an interactive vehicle recommender system based a novel machine learning method that integrates decision trees and multi-armed bandits. Decision tree learning effectively selects important questions to ask the customer and encodes the customer's key preferences. With these preferences as prior information, the multi-armed bandit algorithm, using Thompson sampling, efficiently leverages the user’s feedback to improve the recommendations in an online fashion. The empirical results show that…
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Reliability-Based Design Optimization with Model Bias and Data Uncertainty

SAE International Journal of Materials and Manufacturing

Ford Motor Company-Yan Fu, Ren-Jye Yang
Northwestern University-Zhen Jiang, Wei Chen
  • Journal Article
  • 2013-01-1384
Published 2013-04-08 by SAE International in United States
Reliability-based design optimization (RBDO) has been widely used to obtain a reliable design via an existing CAE model considering the variations of input variables. However, most RBDO approaches do not consider the CAE model bias and uncertainty, which may largely affect the reliability assessment of the final design and result in risky design decisions. In this paper, the Gaussian Process Modeling (GPM) approach is applied to statistically correct the model discrepancy which is represented as a bias function, and to quantify model uncertainty based on collected data from either real tests or high-fidelity CAE simulations. After the corrected model is validated by extra sets of test data, it is integrated into the RBDO formulation to obtain a reliable solution that meets the overall reliability targets while considering both model and parameter uncertainties. The proposed technique is demonstrated through a vehicle design problem aiming at minimizing the vehicle weight through gauge optimization while satisfying reliability constraints. The RBDO result considering model uncertainty is compared with the one from conventional RBDO to demonstrate the benefits of the…
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