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| Title: | BeCross：融合個人行為偏誤的推薦系統建模方法
BeCross: A Personalized Behavior Bias Modeling Layer for Deep Recommender Systems |
| Authors: | 楊宗泰
Yang, Tsung-Tai |
| Contributors: | 徐士勛
蔡炎龍
Hsu, Shih-Hsun
Tsai, Yen-lung
楊宗泰
Yang, Tsung-Tai |
| Keywords: | 推薦系統
行為經濟學交互推薦系統
行為經濟學
個人化
Recommendation system
BeCrossRec
Behavior economics
Personalized |
| Date: | 2025 |
| Issue Date: | 2025-08-04 12:48:48 (UTC+8) |
| Abstract: | 推薦系統自引入深度學習以來持續蓬勃發展。Neural Collaborative Filtering （NCF） 將協同過濾嵌入深度架構以學習非線性交互；Deep Crossing 促成特徵自動組合；Wide & Deep 結合記憶與泛化能力；DIN 引入注意力機制用以學習用戶歷史行為與目標項目的關聯；MaskNet 則修正了 DNN 對於乘法交互表達力不足的問題。這些研究皆回應了實務上推薦系統面臨的挑戰，並透過架構設計不斷提升模型能力。推薦系統本質是一種協助人類決策的工具，過去研究多未考慮用戶決策本身所蘊含的偏誤與捷思。根據行為經濟學理論，人在面對決策選擇時常受錨定效應、可得性捷思等影響，例如用戶觀看 Netflix 的漫威影集時，其期待值會受到過往觀看經驗的影響，並在此基礎上進行調整。然而若推薦模型未能捕捉這類心理偏誤，將導致重要變數遺漏與預測偏差。為此，本文提出 BeCrossRec 架構，在現有深度學習推薦架構中，嵌入行為經濟學特徵交互模組，並透過特徵化、個人化捷思與偏誤，使模型得以捕捉多種個人化行為偏誤之複雜規則，並於實驗結果發現有效提升預測準確度。此設計不僅納入人性決策捷思與偏誤，也為未來結合行為經濟學理論與推薦系統研究，提供可行之方向。
Since the introduction of deep learning, recommender systems have undergone rapid and sustained advancement. Neural Collaborative Filtering integrates collaborative filtering into deep architectures to capture nonlinear interactions; Deep Crossing enables automatic feature combination; Wide & Deep networks balance memorization and generalization capabilities; Deep Interest Network introduces attention mechanisms to model the relationship between user history and target items; and MaskNet addresses the limitations of DNNs in expressing multiplicative interactions. These approaches have tackled real-world challenges in recommendation tasks and continuously improved algorithmic performance through architectural innovations. Fundamentally, recommender systems serve as tools to assist human decision-making. However, most existing studies have overlooked the cognitive biases and heuristics that influence user decisions. According to behavioral economics, individuals are often affected by phenomena such as the anchoring effect and availability heuristic when making choices. For example, a user's expectations while watching a Marvel series on Netflix may be shaped by their prior viewing experiences, which serve as anchors for current judgments. If recommendation models fail to capture such psychological biases, they risk omitting critical variables and introducing prediction bias. To address this, we propose BeCrossRec, a novel architecture that integrates a behavioral economics feature interaction module into existing deep recommendation frameworks. By explicitly modeling heuristics and biases—both in general and in personalized forms—BeCrossRec enables the learning of complex rules underlying individual behavioral biases. Experimental results demonstrate that this approach significantly improves predictive accuracy. This design not only incorporates human decision-making tendencies into recommendation modeling but also offers a viable direction for future research at the intersection of behavioral economics and recommender systems. |
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| Description: | 碩士
國立政治大學
經濟學系
108258037 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108258037 |
| Data Type: | thesis |
| Appears in Collections: | [經濟學系] 學位論文
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