時間：12月6日（星期一）下午15:00-16:30
騰訊會議號：117 564 951
報告人：西安交通大學劉佳鵬 副教授
主講人簡介：
劉佳鵬博士，西安交通大學偉德國際1946bv官網智能決策與機器學習研究中心副教授、博士生導師。目前的研究方向包括：決策分析、機器學習、貝葉斯方法、大數據模型。主持過國家自然科學基金青年項目及面上項目、國家重點研發計劃項目子課題以及博士后科學基金項目。研究成果發表在INFORMS Journal on Computing、European Journal of Operational Research、Omega、Knowledge-based Systems、系統工程理論與實踐、系統工程學報等國內外重要學術期刊。現擔任中國優選法統籌法與經濟數學研究會智能決策與博弈分會理事、中國系統工程學會數據科學與知識系統工程專委會委員。
報告內容簡介：
We propose a preference learning algorithm for uncovering Decision Makers’(DMs’) contingent evaluation strategies in the context of multiple criteria sorting. We assume the preference information in the form of holistic assignment examples derived from the analysis of alternatives’ performance vectors and textual descriptions. We characterize the decision policies using a mixture of threshold-based value-driven preference models and associated latent topics. The latter serve as the stimuli underlying the contingency in decision behavior, providing a transparent and interpretable way to explore and understand DMs’ contingent preferences. Such a probabilistic model is constructed using a flexible and nonparametric Bayesian framework. The proposed method adopts a hierarchical Dirichlet process so that a group of DMs can share a countably infinite number of contingent models and topics. For all DMs, it automatically identifies the components representing their evaluation strategies adequately. The posterior is summarized using the Hamiltonian Monte Carlo sampling method. We demonstrate the method’s practical usefulness on a real-world recruitment problem considered by a Chinese IT company. We discuss the contingent models and topics and illustrate their employment for classifying the job applicants. We also compare the approach with counterparts that use just a single preference model, implement the parametric framework, or consider each DM’s preferences individually.
（承辦：管理工程系、科研與學術交流中心）
