基於混合過濾的電影推薦系統

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黃少邦(Shao-Pan Huang) 查詢紙本館藏

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資訊管理學系

論文名稱

基於混合過濾的電影推薦系統
(Movie Recommender System: A Hybrid Approach)

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至系統瀏覽論文 (2028-6-29以後開放)

摘要(中)

隨著OTT平台的快速發展，在平台上的資訊過於龐大，使用者需要花費額外的時間進行選擇和分析所需內容。對平台方而言，因為以訂閱制提供服務，所以維持使用者對平台的興趣，以避免使用者流失，因此使用推薦系統輔助消費者選擇。在推薦系統領域，混合過濾（Hybrid filtering）被廣泛應用，以解決單一推薦算法所帶來的問題。然而，當前大多數研究專注於推薦系統的準確性，對於提高多樣性的探討相對較少。因此，本研究結合兩種不同的推薦算法，專注於OTT平台電影推薦應用的研究。比較不同混合方式對推薦系統多樣性與準確性的影響。研究旨在探討利用協同過濾方法改善內容基礎過濾推薦多樣性的效果，並使用MovieLens與TMDB資料集所結合的資料作為實驗使用。最終，本研究將提出一個在多樣性和準確性方面平衡且具有應用價值的推薦系統，以提高OTT平台的使用體驗和用戶忠誠度。

摘要(英)

With the rapid development of OTT platforms, users face the challenge of information overload, requiring additional time to select and analyze the desired content. Furthermore, many OTT platforms adopt subscription-based models, making the accuracy and diversity of recommendation systems crucial for maintaining user interest in the platform. In the field of recommendation systems, hybrid filtering is widely used to address the issues arising from employing a single recommendation algorithm. However, the majority of current research focuses on the accuracy of recommendation systems, with relatively less exploration on enhancing diversity. Therefore, this study combines two different recommendation algorithms, focusing on the application of movie recommendations on OTT platforms. Building upon collaborative filtering as the foundation, content-based filtering is used to extend the existing recommendation list, comparing the effects of different hybrid approaches on the accuracy and diversity of the recommendation system. The objective of this study is to explore the efficacy of enhancing the diversity in recommendations generated by content-based filtering by incorporating collaborative filtering techniques. Ultimately, this study propose a recommendation system that achieves a balance between accuracy and diversity, with broad applicability, to enhance the user experience and loyalty on OTT platforms.

關鍵字(中)

★ 推薦系統
★ OTT平台
★ 混合過濾
★ 多樣性

關鍵字(英)

★ Recommendation system
★ OTT platform
★ Hybrid filtering
★ Diversity

論文目次

Chinese Abstract i
Abstract ii
Acknowledgements iii
Table of Contents iv
List of Figures vi
List of Tables vii
I. Introduction 1
1-1 Research Background 1
1-2 Research Motivation 5
1-3 Research Objectives 9
1-4 Research Structure 10
II. Literature Review 13
2-1 Recommender System 13
2-1-1 Collaborative Filtering 16
2-1-2 Content-based Filtering 18
2-1-3 Hybrid Filtering 20
2-2 Collaborating Filtering System based on SVD 22
2-3 Content-based Filtering System based on Language Model 23
2-4 Diversity in Recommendation System 24
III. Methodology 26
3-1 Research Design 26
3-2 Data Collection 28
3-3 Data Preprocessing 31
3-4 Singular Value Decomposition 34
3-5 Bidirectional Encoder Representations from Transformers 36
3-6 TopN Recommendation 38
3-7 Evaluation 38
3-7-1 Hit Rate 39
3-7-2 Diversity Metrics 40
IV. Research Result and Discussion 41
4-1 Data Visualization 41
4-2 Diversity-oriented recommendation results 47
4-3 Recommendation results with both diversity and accuracy 49
4-4 Recommendation results with different movies popularity 51
V. Research Conclusion and Contribution 58
5-1 Conclusion 58
5-1-1 Impact of Combining CF into CBF Recommendation System 58
5-1-2 Impact of Hybrid recommendations on accuracy and diversity 59
5-1-3 Impact of movie popularity on recommendations 60
5-2 Research Limitations and Future Directions 61
References 63

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指導教授

曾筱珽(Hsiao-Ting Tseng)

審核日期

2023-6-29

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