基於混合過濾的電影推薦系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：119

、訪客IP：3.143.17.50

姓名

黃少邦(Shao-Pan Huang) 查詢紙本館藏

畢業系所

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)

相關論文

★ 以機器學習技術為基礎建構新生兒孕育健康狀態預測模型	★ 電子病歷縮寫消歧與一對多分類任務
★ 使用文字探勘與深度學習技術建置中風後肺炎之預測模型	★ 精準社群廣告投資策略：以機器學習技術為基礎之社會影響力管理模式
★ 智慧活躍老化之實現：以資料驅動為基礎之AI長者在地交友推薦模式	★ 整合深度學習技術與SOR理論之資訊情緒傳遞性探索：新聞生成特質與資訊情感傳播行為
★ 以 Reddit 使用者生成內容探討糖尿病照護社會支持	★ 防範於未然：基於機器學習技術之網路入侵偵測系統
★ 可靠度驗證實驗室導入人工智慧技術的可行性探討 -以A公司為例	★ 智慧共同照護之實現: 以資料驅動為基礎之 AI 糖尿病個案管理模式
★ Non-Touch Cooperation: An Interactive Mechanism Design Based on Mid-Air Gestures	★ 基於 UGC 的反脆弱社交口碑策略:以臺灣飯店業為例
★ 基於機器學習技術之誘導式評論過濾機制：以餐廳評論為例	★ 資料驅動的球隊經營：NBA球隊競爭力與抱團策略之研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

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

參考文獻

Abbasi-Moud, Z., Vahdat-Nejad, H., & Sadri, J. (2021). Tourism recommendation system based on semantic clustering and sentiment analysis. Expert Systems with Applications, 167, 114324. https://doi.org/https://doi.org/10.1016/j.eswa.2020.114324
Adamopoulos, P., Ghose, A., & Tuzhilin, A. (2022). Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments. MIS Quarterly, 46(1), 101-150. https://doi.org/10.25300/MISQ/2021/15611
Adamopoulos, P., & Tuzhilin, A. (2014). On Unexpectedness in Recommender Systems: Or How to Better Expect the Unexpected. ACM Transactions on Intelligent Systems and Technology, 5(4), Article 54. https://doi.org/10.1145/2559952
Adomavicius, G., & Kwon, Y. (2012). Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques. IEEE Transactions on Knowledge and Data Engineering, 24(5), 896-911. https://doi.org/10.1109/TKDE.2011.15
Afoudi, Y., Lazaar, M., & Al Achhab, M. (2021). Hybrid recommendation system combined content-based filtering and collaborative prediction using artificial neural network. Simulation Modelling Practice and Theory, 113, 102375. https://doi.org/10.1016/j.simpat.2021.102375
Ahuja, R., Solanki, A., & Nayyar, A. (2019). Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor. 2019 9th International Conference on Cloud Computing, Data Science & Engineering. January 10-11 2019, Noida, India. https://doi.org/10.1109/CONFLUENCE.2019.8776969
Albayati, A. N. K., & Ortakci, Ö. Ü. Y. (2022). Recommendation Systems on Twitter Data for Marketing Purposes using Content-Based Filtering. 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). June 09 - 11 2022 ,Ankara, Turkey. https://doi.org/10.1109/HORA55278.2022.9799989
Aljunid, M. F., & Dh, M. (2020). An Efficient Deep Learning Approach for Collaborative Filtering Recommender System. Procedia Computer Science, 171, 829-836. https://doi.org/10.1016/j.procs.2020.04.090
Asani, E., Vahdat-Nejad, H., & Sadri, J. (2021). Restaurant recommender system based on sentiment analysis. Machine Learning with Applications, 6, 100114. https://doi.org/10.1016/j.mlwa.2021.100114
Barrera, N., Torres, R., Rodríguez, J., Espinosa, O., Avellaneda, S., & Ramírez, J. (2023). A recommender system for occupational hygiene services using natural language processing. Healthcare Analytics, 100148. https://doi.org/10.1016/j.health.2023.100148
Bezerra, B., de Carvalho, F. d. A., Ramalho, G. L., & Zucker, J.-D. (2003). Speeding up recommender systems with meta-prototypes. Advances in Artificial Intelligence: 16th Brazilian Symposium on Artificial Intelligence, SBIA 2002 Porto de Galinhas/Recife. November 11–14 2002, Brazil. https://doi.org/10.1007/3-540-36127-8_22
Bilton, N. (2009). The American Diet: 34 Gigabytes a Day. The New York Times. https://www.nytimes.com/2009/12/10/technology/10data.html
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109-132. https://doi.org/10.1016/j.knosys.2013.03.012
Bouazza, H., Said, B., & Zohra Laallam, F. (2022). A hybrid IoT services recommender system using social IoT. Journal of King Saud University - Computer and Information Sciences, 34(8, Part B), 5633-5645. https://doi.org/10.1016/j.jksuci.2022.02.003
Burke, R. (2002). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User-Adapted Interaction, 12(4), 331-370. https://doi.org/10.1023/A:1021240730564
Burke, R. (2007). Hybrid Web Recommender Systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), The Adaptive Web: Methods and Strategies of Web Personalization (pp. 377-408). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_12
Cataltepe, Z., Uluyagmur, M., & Tayfur, E. (2016). Feature selection for movie recommendation. Turkish Journal of Eletriccal Engineering and Ccomputer Sciences, 24(3), 833-848. https://doi.org/10.3906/elk-1303-189
Chai, Z. Y., Li, Y. L., Han, Y. M., & Zhu, S. F. (2019). Recommendation System Based on Singular Value Decomposition and Multi-Objective Immune Optimization. IEEE Access, 7, 6060-6071. https://doi.org/10.1109/ACCESS.2018.2842257
Channarong, C., Paosirikul, C., Maneeroj, S., & Takasu, A. (2022). HybridBERT4Rec: A Hybrid (Content-Based Filtering and Collaborative Filtering) Recommender System Based on BERT. IEEE Access, 10, 56193-56206. https://doi.org/10.1109/ACCESS.2022.3177610
Chaudhary, S. (2019). Why “1.5” in IQR Method of Outlier Detection? Shivam Chaudhary. https://towardsdatascience.com/why-1-5-in-iqr-method-of-outlier-detection-5d07fdc82097
Edmunds, A., & Morris, A. (2000). The problem of information overload in business organisations: a review of the literature. International Journal of Information Management, 20(1), 17-28. https://doi.org/https://doi.org/10.1016/S0268-4012(99)00051-1
Fang, W., Sha, Y., Qi, M. H., & Sheng, V. S. (2022). Movie Recommendation Algorithm Based on Ensemble Learning. Intellgent Automation and Soft Computing, 34(1), 609-622. https://doi.org/10.32604/iasc.2022.027067
Fararni, K. A., Nafis, F., Aghoutane, B., Yahyaouy, A., Riffi, J., & Sabri, A. (2021). Hybrid recommender system for tourism based on big data and AI: A conceptual framework. Big Data Mining and Analytics, 4(1), 47-55. https://doi.org/10.26599/BDMA.2020.9020015
Farooq, M., & Raju, V. (2019). Impact of over-the-top (OTT) services on the telecom companies in the era of transformative marketing. Global Journal of Flexible Systems Management, 20(2), 177-188. https://doi.org/10.1007/s40171-019-00209-6
Fleder, D. M., & Hosanagar, K. (2007). Recommender systems and their impact on sales diversity. In Proceedings of the 8th ACM conference on Electronic commerce. June 11-15 2007, San Diego, California, USA. https://doi.org/10.1145/1250910.1250939
Fu, S., Li, H., Liu, Y., Pirkkalainen, H., & Salo, M. (2020). Social media overload, exhaustion, and use discontinuance: Examining the effects of information overload, system feature overload, and social overload. Information Processing & Management, 57(6), 102307. https://doi.org/10.1016/j.ipm.2020.102307
Ge, M., Delgado-Battenfeld, C., & Jannach, D. (2010). Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. September 26 - 30 2010, Barcelona, Spain. https://doi.org/10.1145/1864708.1864761
Ghasemi, N., & Momtazi, S. (2021). Neural text similarity of user reviews for improving collaborative filtering recommender systems. Electronic Commerce Research and Applications, 45, 101019. https://doi.org/10.1016/j.elerap.2020.101019
Goldberg, D., Nichols, D., Oki, B. M., & Terry, D. (1992). Using Collaborative Filtering to Weave an Information Tapestry. Commun. ACM, 35(12), 61–70. https://doi.org/10.1145/138859.138867
Gomez-Uribe, C. A., & Hunt, N. (2016). The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Transactions on Management Information Systems, 6(4). https://doi.org/10.1145/2843948
Grand-View-Research. (2019). Recommendation Engine Market Size, Share & Trends Analysis Report By Type (Collaborative Filtering, Hybrid Recommendation), By Deployment, By Application, By Organization, By End-use, By Region, And Segment Forecasts, 2021 - 2028. https://www.grandviewresearch.com/industry-analysis/recommendation-engine-market-report
Harper, F. M., & Konstan, J. A. (2015). The MovieLens Datasets: History and Context.
ACM Transactions on Management Information Systems, 5(4). https://doi.org/10.1145/2827872
Hassan, H. A. M., Sansonetti, G., Gasparetti, F., Micarelli, A., & Beel, J. (2019). BERT, ELMo, USE and InferSent Sentence Encoders: The Panacea for Research-Paper Recommendation, ACM Conference on Recommender Systems.
http://ceur-ws.org/Vol-2431/paper2.pdf
Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Management Information Systems, 22(1), 5–53. https://doi.org/10.1145/963770.963772
Hu, Rong, & Pu, Pearl. (2011). Helping users perceive recommendation diversity. DiveRS@ RecSys, ceur-ws.org/Vol-816/paper6.pdf
Hu, Y. T., Xiong, F., Lu, D. Y., Wang, X. M., Xiong, X., & Chen, H. S. (2020). Movie collaborative filtering with multiplex implicit feedbacks. Neurocomputing, 398, 485-494. https://doi.org/10.1016/j.neucom.2019.03.098
Jacob, Devlin, Ming-Wei, C., Kenton, L., & Kristina, T. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. https://doi.org/10.48550/arXiv.1810.04805
Jain, Kaneenika. (2021). The Rise of OTT Platform: Changing Consumer Preferences. EPRA International Journal of Multidisciplinary Research (IJMR), 7(6), 257-261. A2504040110.pdf (iosrjournals.org)
Jain, S., Grover, A., Thakur, P. S., & Choudhary, S. K. (2015). Trends, problems and solutions of recommender system. International Conference on Computing, Communication & Automation. May 15-16 2015, Greater Noida, India.
https://www.doi.org/10.1109/CCAA.2015.7148534
Jung, J., & Melguizo, Á. (2023). Is your netflix a substitute for your telefunken? Evidence on the dynamics of traditional pay TV and OTT in Latin America. Telecommunications Policy, 47(1), 102397. https://doi.org/10.1016/j.telpol.2022.102397
Kaminskas, M., & Bridge, D. (2016). Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems. ACM Transactions on Management Information Systems, 7(1), Article 2. https://doi.org/10.1145/2926720
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012). Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction, 22(4), 441-504. https://doi.org/10.1007/s11257-011-9118-4
Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics, 11(1). https://www.mdpi.com/2079-9292/11/1/141
Koren, Y. (2009). Collaborative filtering with temporal dynamics. Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. 2009 Paris, France. https://doi.org/10.1145/1557019.1557072
Koren, Y., Rendle, S., & Bell, R. (2022). Advances in Collaborative Filtering. In F. Ricci, L. Rokach, & B. Shapira (Eds.), Recommender Systems Handbook (pp. 91-142). Springer US. https://doi.org/10.1007/978-1-0716-2197-4_3
Krikke, J. (2004). Streaming video transforms the media industry. IEEE Computer Graphics and Applications, 24(4), 6-12. https://doi.org/10.1109/MCG.2004.17
Kumar, S., De, K., & Roy, P. P. (2020). Movie Recommendation System Using Sentiment Analysis From Microblogging Data [Article]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, 7(4), 915-923. https://doi.org/10.1109/tcss.2020.2993585
Kunaver, M., & Požrl, T. (2017). Diversity in recommender systems – A survey. Knowledge-Based Systems, 123, 154-162. https://doi.org/https://doi.org/10.1016/j.knosys.2017.02.009
Li, X., & Murata, T. (2012, 4-7 Dec. 2012). Using Multidimensional Clustering Based Collaborative Filtering Approach Improving Recommendation Diversity. 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology,
Liang, T.-P., Lai, H.-J., & Ku, Y.-C. (2006). Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings. Journal of Management Information Systems, 23(3), 45-70. https://doi.org/10.2753/MIS0742-1222230303
Liao, M., & Sundar, S. S. (2022). When E-Commerce Personalization Systems Show and Tell: Investigating the Relative Persuasive Appeal of Content-Based versus Collaborative Filtering. Journal of Advertising, 51(2), 256-267. https://doi.org/10.1080/00913367.2021.1887013
LLP, M. I. (2023). Recommendation Engine Market - Growth, Trends, COVID-19 Impact, and Forecasts (2023 - 2028). M. I. LLP. Recommendation Engine Market - Growth, Trends, COVID-19 (globenewswire.com)
Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12-32. https://doi.org/10.1016/j.dss.2015.03.008
Mahdi, M. N., Ahmad, A. R., Ismail, R., Subhi, M. A., Abdulrazzaq, M. M., & Qassim, Q. S. (2020). Information Overload: The Effects of Large Amounts of Information. 2020 1st. Information Technology To Enhance e-learning and Other Application. July 12-13 2020, Baghdad, Iraq. https://www.doi.org/10.1109/IT-ELA50150.2020.9253082
Marcuzzo, M., Zangari, A., Albarelli, A., & Gasparetto, A. (2022). Recommendation Systems: An Insight Into Current Development and Future Research Challenges. IEEE Access, 10, 86578-86623. https://doi.org/10.1109/Access.2022.3194536
Mathew, P., Kuriakose, B., & Hegde, V. (2016). Book Recommendation System through content based and collaborative filtering method. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE). March 16-18 2016, Ernakulam, India. https://www.doi.org/10.1109/SAPIENCE.2016.7684166
Mishra, R. K., Urolagin, S., & J, A. A. J. (2019). A Sentiment analysis-based hotel recommendation using TF-IDF Approach. 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE).
December 11-12 2019, Dubai, United Arab Emirates. https://www.doi.org/10.1109/ICCIKE47802.2019.9004385
Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A. H. (2020). Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Systems with Applications, 149, Article 113248. https://doi.org/10.1016/j.eswa.2020.113248
NCC. (2016). 2016 Performance Report. https://www.ncc.gov.tw/english/files/18022/382_2184_180227_1.pdf
Ozok, A. A., Fan, Q., & Norcio, A. F. (2010). Design guidelines for effective recommender system interfaces based on a usability criteria conceptual model: results from a college student population. Behaviour & Information Technology, 29(1), 57-83. https://www.doi.org/10.1080/01449290903004012
Park, S., & Kwon, Y. (2019). Research on the Relationship between the Growth of OTT Service Market and the Change in the Structure of the Pay-TV Market. 30th European Conference of the International Telecommunications Society (ITS). June 16-19 2019, Helsinki, Finland. https://www.econstor.eu/handle/10419/205203
Park, S.-H., & Han, S. P. (2012). Empirical analysis of the impact of product diversity on long-term performance of recommender systems. Proceedings of the 14th Annual International Conference on Electronic Commerce, https://doi.org/10.1145/2346536.2346592
Pujahari, A., & Sisodia, D. S. (2022). Item feature refinement using matrix factorization and boosted learning based user profile generation for content-based recommender systems. Expert Systems with Applications, 206, 117849. https://doi.org/ 10.1016/j.eswa.2022.117849
Qing, L., & Kim. (2003). Clustering approach for hybrid recommender system.In Proceedings IEEE/WIC International Conference on Web Intelligence, December 14 - 17 2003, Melbourne, Australia. https://doi.org/10.1109/WI.2003.1241167
Ranjan, A. A., Rai, A., Haque, S., Lohani, B. P., & Kushwaha, P. K. (2019). An approach for Netflix recommendation system using singular value decomposition. Journal of Computer and Mathematical Sciences, 10(4), 774-779. http://www.compmath-journal.org/dnload/Ankur-A-Ranjan-Amod-Rai-Saiful-Haque-Bhanu-P-Lohani4and-Pradeep-K-Kushwaha5/CMJV10I04P0774.pdf
Rhanoui, M., Mikram, M., Yousfi, S., Kasmi, A., & Zoubeidi, N. (2022). A hybrid recommender system for patron driven library acquisition and weeding. Journal of King Saud University - Computer and Information Sciences, 34(6, Part A), 2809-2819. https://doi.org/10.1016/j.jksuci.2020.10.017
Robin van, M. (2000). Using Content-Based Filtering for Recommendation. In Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop (Vol. 30, pp. 47-56). http://users.ics.forth.gr/~potamias/mlnia/paper_6.pdf
Salter, J., & Antonopoulos, N. (2006). CinemaScreen recommender agent: combining collaborative and content-based filtering. IEEE Intelligent Systems, 21(1), 35-41. https://doi.org/10.1109/MIS.2006.4
Salton, G. (1983). Introduction to modern information retrieval. McGraw-Hill. https://dl.acm.org/doi/abs/10.5555/576628
Shambour, Q. Y., Abu-Shareha, A. A., & Abualhaj, M. M. (2022). A Hotel Recommender System Based on Multi-Criteria Collaborative Filtering. Information Technology and Control, 51(2), 390-402. https://doi.org/10.5755/j01.itc.51.2.30701
Shandilya, R., Sharma, S., & Wong, J. (2022). MATURE-Food: Food Recommender System for MAndatory FeaTURE Choices A system for enabling Digital Health. International Journal of Information Management Data Insights, 2(2), 100090. https://doi.org/10.1016/j.jjimei.2022.100090
Sharma, B., Hashmi, A., Gupta, C., Khalaf, O. I., Abdulsahib, G. M., & Itani, M. M. (2022). Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System. Symmetry-Basel, 14(4), 16, Article 793. https://doi.org/10.3390/sym14040793
Sivamol, S., & Suresh, K. (2019). Personalization Phenom: User-centric Perspectives towards Recommendation Systems in Indian Video Services. SCMS Journal of Indian Management, 16(2), 73-86.

Son, J., & Kim, S. B. (2017). Content-based filtering for recommendation systems using multiattribute networks. Expert Systems with Applications, 89, 404-412. https://doi.org/https://doi.org/10.1016/j.eswa.2017.08.008
Su, X., & Khoshgoftaar, T. M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009. https://doi.org/10.1155/2009/421425
Tandoc, E. C., & Kim, H. K. (2022). Avoiding real news, believing in fake news? Investigating pathways from information overload to misbelief. Journalism, 14648849221090744. https://doi.org/10.1177/14648849221090744
Thorat, P., Goudar, R., & Barve, S. (2015). Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System. International Journal of Computer Applications, 110, 31-36. https://doi.org/10.5120/19308-0760
TMDb. (2017). The Movie Database (TMDb). The Movie Database (TMDb). https://www.themoviedb.org/
Vargas, S. (2011). New approaches to diversity and novelty in recommender systems. Fourth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2011) (FDIA). https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/FDIA2011.2
Vargas, S., Baltrunas, L., Karatzoglou, A., & Castells, P. (2014). Coverage, redundancy and size-awareness in genre diversity for recommender systems. Proceedings of the 8th ACM Conference on Recommender systems,
Vargas, S., & Castells, P. (2011). Rank and relevance in novelty and diversity metrics for recommender systems Proceedings of the fifth ACM conference on Recommender systems, Chicago, Illinois, USA. https://doi.org/10.1145/2043932.2043955
Vozalis, M. G., & Margaritis, K. G. (2005). Applying SVD on item-based filtering. 5th International Conference on Intelligent Systems Design and Applications (ISDA05),
September 8-10 2005, Watsaw. https://doi.org/10.1109/ISDA.2005.25
Walek, B., & Fojtik, V. (2020). A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications, 158, 113452. https://doi.org/10.1016/j.eswa.2020.113452
Wei, Sha, Y., Qi, M., & Sheng, V. S. (2022). Movie Recommendation Algorithm Based on Ensemble Learning. Intelligent Automation & Soft Computing, 609-622. https://doi.org/10.32604/iasc.2022.027067
Yadalam, T. V., Gowda, V. M., Kumar, V. S., Girish, D., & N, M. (2020). Career Recommendation Systems using Content based Filtering. 2020 5th International Conference on Communication and Electronics Systems (ICCES), June 10-12 2020, Coimbatore, India. https://doi.org/10.1109/ICCES48766.2020.9137992
Yang, N., Jo, J., Jeon, M., Kim, W., & Kang, J. (2022). Semantic and explainable research-related recommendation system based on semi-supervised methodology using BERT and LDA models. Expert Systems with Applications, 190, 116209. https://doi.org/ 10.1016/j.eswa.2021.116209
Yuan, Lixin, H., Subin, Q., Guoxia, X., & Hong, Y. (2019). Singular value decomposition based recommendation using imputed data. Knowledge-Based Systems, 163, 485-494. https://doi.org/10.1016/j.knosys.2018.09.011
Zagranovskaia, A., & Mitura, D. (2022). Designing Hybrid Recommender Systems. IV International Scientific and Practical Conference, 1-5. https://doi.org/10.1145/3487757.3490921
Zhang, M., & Hurley, N. (2008). Avoiding monotony: improving the diversity of recommendation lists.In Proceedings of the 2008 ACM conference on Recommender systems, 2008, Lausanne, Switzerland. https://doi.org/10.1145/1454008.1454030
Zins, A. H., & Bauernfeind, U. (2005). Explaining online purchase planning experiences with recommender websites. In Information and Communication Technologies in Tourism 2005 (pp. 137-148). https://doi.org/10.1007/3-211-27283-6_13

指導教授

曾筱珽(Hsiao-Ting Tseng)

審核日期

2023-6-29

推文