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| Title: | 演算法定價與雙目標深度學習
Algorithmic Pricing and Dual-Objective Deep Learning |
| Authors: | 林子紘
Lin, Zi-Hong |
| Contributors: | 莊皓鈞
周彥君
Chuang, Hao-Chun
Chou, Yen-Chun
林子紘
Lin, Zi-Hong |
| Keywords: | 動態定價
客製化損失函數
神經網路定價
Dynamic Pricing
Customized Loss Function
Neural Network Pricing |
| Date: | 2024 |
| Issue Date: | 2025-03-03 15:10:15 (UTC+8) |
| Abstract: | 動態定價被視為最基本與最常用的收益管理工具之一，過往具有許多不同層面的研究。隨著網路科技的發展，動態定價研究在取得消費者資訊和調整價格成本方面的進步，動態定價運用在個人化定價的應用也越來越廣泛，能提供消費者更適合的價格，從而提高整體收益。然而，準確了解消費者需求並推薦合適價格是一項挑戰，但隨著新技術的發展，透過機器學習與深度學習可以更全面地且更精準地進行動態定價。
本研究首先比較了兩種主要的動態定價方法：兩階段模型和客製化損失函數。我們使用實證資料以及模擬資料進行比較及分析，並提出一改良的損失函數——Dynamic Airbnb Loss，與其他方法進行比較，證實了該損失函數在參數設定上更直觀且效果更佳的優勢。
本研究之另一重點為根據消費者的價值評估函數理論基礎發想，提出了一雙目標之神經網路模型。我們利用神經網路的彈性設計優勢，透過同時預測價格以及該價格之購買機率的方法，並且加入神經網路正規化概念，藉此能夠推薦接近消費者最大願付價格的價格。我們將此架構運用在模擬資料中，透過獲利以及不同的評估指標，證實我們所提出的雙目標神經網路是具有可行性也具有相當的潛力。
Dynamic pricing is regarded as one of the most fundamental revenue management tools, with extensive research from various perspectives. With advancements in technology, dynamic pricing research has made significant progress in obtaining consumer information and reducing price adjustment costs. Personalized pricing applications of dynamic pricing are becoming widespread, offering consumers more suitable prices and enhancing overall revenue. However, accurately understanding consumer demand and recommending appropriate prices is challenging. Machine learning and deep learning enable more comprehensive and precise dynamic pricing.
This research compares two main dynamic pricing methods: the two-stage model and the model using a customized loss function. We conduct analysis using empirical and simulated data and propose an improved loss function, the Dynamic Airbnb Loss. Compared to other methods, this new loss function demonstrates advantages in being more intuitive in parameter settings and more effective in application.
Another key focus of this research is a dual-objective neural network model based on the theoretical foundation of consumer value assessment functions. Leveraging the flexibility of neural network design, we simultaneously predict prices and the probability of purchase at those prices with the concept of neural network regularization. This approach allows us to recommend prices that are close to the consumer’s maximum willingness to pay. We apply this framework to simulated data and evaluate it through profitability and various performance metrics, confirming that the proposed dual-objective neural network is both feasible and highly promising. |
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| Description: | 碩士
國立政治大學
資訊管理學系
111356014 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111356014 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
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