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題名 基於增強學習的直播電商推薦系統
Reinforcement learning based live streaming e-commerce recommender system作者 唐思琪
Tang, Szu-Chi貢獻者 林怡伶
Ling, Yi-Lin
唐思琪
Tang, Szu-Chi關鍵詞 直播電商
推薦系統
強化學習
探索與利用之權衡
神經網路
Live commerce
Live streaming
E-commerce
Recommender system
Recommendation system
Multi-armed bandit
Reinforcement learning
User context
Uncertainty
Exploitation-exploration trade-off
Gated Recurrent Unit
Variational Autoencoder
Bayesian neural networks日期 2022 上傳時間 1-Aug-2022 17:20:44 (UTC+8) 摘要 近年來，直播電商逐漸受到重視。不同於傳統的電商和單向推播的電視購物，直播電商更加強調即時互動性。由於開設直播的成本低，直播主發起直播的頻率很高、商品也是不斷推成出新，這些都促成了複雜且快速變動的環境，而推薦系統能夠幫助消費者在資訊爆炸的情況下快速做出決定。過往的推薦系統研究注重於準確率的最佳化，不只引發了同溫層效應，更因為總是推薦類似的商品，長期下來導致消費者的不滿意以及流失。為了在精準推薦與探索新喜好的取捨中獲得較好的平衡，我們將此議題看作是一個具備使用者情境的多臂吃角子老虎機問題。此研究在直播電商這種新的商業情境下，提出一個基於強化學習的推薦系統。它能夠通過靜態的顧客特徵以及具時序性的顧客特徵，找出顧客、直播主以及商品之間的關係。我們使用了一種循環神經網路——門基循環單元，來找出顧客隨時間變化的喜好。我們的直播電商推薦系統能夠藉由變分自動變碼器來模糊化顧客的特徵，並在推薦商品的過程中利用貝葉斯神經網路來引入不確定性，來達成控制探索顧客喜好與利用的平衡。據我們所知，我們是第一個提出以基於神經網路的上下文吃角子老虎機演算法，來解決直播電商平台環境下推薦問題的研究。我們比較了經典的多臂吃餃子老虎機演算法，並透過真實世界資料的實驗來初步驗證了我們的理論，並且展示了其在商業實務問題中的潛在應用。
In recent years, live stream e-commerce shopping has received extensive attention from e-commerce businesses and streaming platforms. Different from traditional TV shopping and online shopping, the emerging products roll out continuously on the live stream shopping platform where users and streamers interact and synchronize in real-time. Such a dynamic environment forms a complex user context. The recommender system plays a crucial role in assisting users in information-seeking tasks and decision-making from information overload. Previous recommender systems mainly focus on optimizing accuracy, which results in filter bubbles problem and high churn rates in the long run. To balance exploration and exploitation (EE) trade-off under a dynamic and fast-changing recommendation context, the research formulates the problem as a contextual bandit problem. This study provides a reinforcement learning (RL)-based solution for a new business scenario (i.e., live stream e-commerce) which addresses three relationships between customers, streamers, and products in both static and temporal user contexts. We use Gated Recurrent Unit (GRU) to model the context changes in users` preferences in streamers and products while maintaining their long-term engagement. By encoded uncertainty in neural networks with Variational Autoencoder (VAE) for user modeling and Bayesian Neural Network (BNN) for a product recommendation, the proposed Live E-commerce Recommender System (LERS) can control the balance of EE trade-off. To the best of our knowledge, our study is the first neural network-based contextual bandit algorithm dealing with the recommendation problem in the live streaming e-commerce platforms. We compared our algorithm with classic multi-armed bandit algorithms including UCB1, LinUCB, Exp3, and NeuralUCB. 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國立政治大學
資訊管理學系
109356002資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356002 資料類型 thesis dc.contributor.advisor 林怡伶 zh_TW dc.contributor.advisor Ling, Yi-Lin en_US dc.contributor.author (Authors) 唐思琪 zh_TW dc.contributor.author (Authors) Tang, Szu-Chi en_US dc.creator (作者) 唐思琪 zh_TW dc.creator (作者) Tang, Szu-Chi en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:20:44 (UTC+8) - dc.date.available 1-Aug-2022 17:20:44 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:20:44 (UTC+8) - dc.identifier (Other Identifiers) G0109356002 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141029 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 109356002 zh_TW dc.description.abstract (摘要) 近年來，直播電商逐漸受到重視。不同於傳統的電商和單向推播的電視購物，直播電商更加強調即時互動性。由於開設直播的成本低，直播主發起直播的頻率很高、商品也是不斷推成出新，這些都促成了複雜且快速變動的環境，而推薦系統能夠幫助消費者在資訊爆炸的情況下快速做出決定。過往的推薦系統研究注重於準確率的最佳化，不只引發了同溫層效應，更因為總是推薦類似的商品，長期下來導致消費者的不滿意以及流失。為了在精準推薦與探索新喜好的取捨中獲得較好的平衡，我們將此議題看作是一個具備使用者情境的多臂吃角子老虎機問題。此研究在直播電商這種新的商業情境下，提出一個基於強化學習的推薦系統。它能夠通過靜態的顧客特徵以及具時序性的顧客特徵，找出顧客、直播主以及商品之間的關係。我們使用了一種循環神經網路——門基循環單元，來找出顧客隨時間變化的喜好。我們的直播電商推薦系統能夠藉由變分自動變碼器來模糊化顧客的特徵，並在推薦商品的過程中利用貝葉斯神經網路來引入不確定性，來達成控制探索顧客喜好與利用的平衡。據我們所知，我們是第一個提出以基於神經網路的上下文吃角子老虎機演算法，來解決直播電商平台環境下推薦問題的研究。我們比較了經典的多臂吃餃子老虎機演算法，並透過真實世界資料的實驗來初步驗證了我們的理論，並且展示了其在商業實務問題中的潛在應用。 zh_TW dc.description.abstract (摘要) In recent years, live stream e-commerce shopping has received extensive attention from e-commerce businesses and streaming platforms. Different from traditional TV shopping and online shopping, the emerging products roll out continuously on the live stream shopping platform where users and streamers interact and synchronize in real-time. Such a dynamic environment forms a complex user context. The recommender system plays a crucial role in assisting users in information-seeking tasks and decision-making from information overload. Previous recommender systems mainly focus on optimizing accuracy, which results in filter bubbles problem and high churn rates in the long run. To balance exploration and exploitation (EE) trade-off under a dynamic and fast-changing recommendation context, the research formulates the problem as a contextual bandit problem. This study provides a reinforcement learning (RL)-based solution for a new business scenario (i.e., live stream e-commerce) which addresses three relationships between customers, streamers, and products in both static and temporal user contexts. We use Gated Recurrent Unit (GRU) to model the context changes in users` preferences in streamers and products while maintaining their long-term engagement. By encoded uncertainty in neural networks with Variational Autoencoder (VAE) for user modeling and Bayesian Neural Network (BNN) for a product recommendation, the proposed Live E-commerce Recommender System (LERS) can control the balance of EE trade-off. To the best of our knowledge, our study is the first neural network-based contextual bandit algorithm dealing with the recommendation problem in the live streaming e-commerce platforms. We compared our algorithm with classic multi-armed bandit algorithms including UCB1, LinUCB, Exp3, and NeuralUCB. Preliminary experiment results on real-world data corroborate our theory and shed light on potential applications of our algorithm to real-world business problems. en_US dc.description.tableofcontents Acknowledgements i摘要 iiAbstract iiiContents vList of Figures viiiList of Tables x1 Introduction 12 RelatedWork 42.1 Live Streaming E-commerce 42.2 Recommender Systems 52.3 Live Streaming Recommender System 62.4 Contextual Multi-armed Bandit Methods 82.5 Uncertainty Modeling 103 The Proposed Framework 123.1 Problem Definition 123.2 Framework Overview 133.3 Gated Recurrent Unit Networks in Temporal Context Model 163.4 Variational Autoencoder for Blurry Context 183.5 Bayesian Neural Networks for Exploring Product Recommendation 203.6 Training Procedure 214 Experiments 254.1 Datasets 254.2 Implementation Environment 254.3 Customer Context Features 264.3.1 Static Context Features 264.3.2 Customer-Product Context Features 264.3.3 Customer-Streamer Context Features 274.4 Temporal Context Modeling 284.4.1 RNN-based Models for Temporal Context 284.4.2 Identify the Appropriate Sequence Length of Temporal Context 294.5 Full Context Analysis 334.6 Dimension Reduction Analysis 354.7 Production Recommendation Analysis 364.7.1 Evaluation Metrics 364.7.2 Experiment Dataset 384.7.3 Recommendation Context for Product Recommendation 404.7.4 Temporal Context for Product Recommendation 424.7.5 End-to-End Live E-commerce Recommender System 444.8 Algorithm Comparison Experiments 454.8.1 Experiments Settings 464.8.2 Normal Dataset 474.8.3 Active Dataset 504.8.4 Repeat Dataset 515 Discussion 545.1 Offline Environment 545.2 Feature Enrichment 545.3 Context Engineering 555.4 Neural Network 556 Conclusion 57References 59 zh_TW dc.format.extent 2478692 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356002 en_US dc.subject (關鍵詞) 直播電商 zh_TW dc.subject (關鍵詞) 推薦系統 zh_TW dc.subject (關鍵詞) 強化學習 zh_TW dc.subject (關鍵詞) 探索與利用之權衡 zh_TW dc.subject (關鍵詞) 神經網路 zh_TW dc.subject (關鍵詞) Live commerce en_US dc.subject (關鍵詞) Live streaming en_US dc.subject (關鍵詞) E-commerce en_US dc.subject (關鍵詞) Recommender system en_US dc.subject (關鍵詞) Recommendation system en_US dc.subject (關鍵詞) Multi-armed bandit en_US dc.subject (關鍵詞) Reinforcement learning en_US dc.subject (關鍵詞) User context en_US dc.subject (關鍵詞) Uncertainty en_US dc.subject (關鍵詞) Exploitation-exploration trade-off en_US dc.subject (關鍵詞) Gated Recurrent Unit en_US dc.subject (關鍵詞) Variational Autoencoder en_US dc.subject (關鍵詞) Bayesian neural networks en_US dc.title (題名) 基於增強學習的直播電商推薦系統 zh_TW dc.title (題名) Reinforcement learning based live streaming e-commerce recommender system en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Allesiardo, R., Féraud, R., & Bouneffouf, D. 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