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題名 應用象徵性資料分析法於電影推薦系統之研究
The application of symbolic data analysis to movie recommendation systems
作者 張順益
CHANG, SHUN-YI
貢獻者 吳漢銘
Wu, Han-Ming
張順益
CHANG, SHUN-YI
關鍵詞 推薦系統
象徵性資料分析法
分群演算法
遺失值補值
Recommendation System
Symbolic Data Analysis
Clustering Algorithm
Missing Value Imputation
日期 2023
上傳時間 1-Sep-2023 14:57:45 (UTC+8)
摘要 推薦系統（Recommendation System）如今已廣泛應用於商業行銷，涵蓋範疇包括電影、音樂、新聞、書籍、餐廳、3C 商品以及金融服務等產品的推薦。推薦系統能為用戶提供精確的個性化推薦，從而提高商家的營利。協同過濾算法（collaborative filtering）\\citep{Resnick} 是推薦算法中最常見的一種，其根據用戶對商品的評分進行協同過濾，以便找出合適的產品進行推薦。該演算法的理論基礎在於消費行為相近的用戶應該會偏好類似的商品。然而，協同過濾算法面臨新用戶冷啟動（亦稱新商品問題）和稀疏矩陣等問題。在本研究中，我們針對電影推薦系統，根據用戶群的特徵將其對電影的評分依照電影類型轉換成多值模態象徵性資料（multi-valued modal symbolic data）。此轉換方法考慮到每部電影可能具有多種類型的特點，旨在克服新用戶冷啟動問題並減少缺失值導致的稀疏矩陣問題。我們進行了模擬實驗並分析了實際的電影評分資料，以驗證我們提出的新方法。結果顯示，應用象徵性資料分析法不僅可以提升推薦的效果，更為推薦系統的發展開創了一條新的思考途徑和方法。
Recommendation systems are now widely used in business marketing, spanning various domains such as movies, music, news, books, restaurants, 3C products, and financial services. Collaborative filtering, the most common recommendation algorithm, utilizes user ratings on products to perform collaborative filtering and identify suitable items for recommendations. The theoretical basis of this algorithm is that users with similar consumption behaviors are likely to prefer similar items. However, collaborative filtering algorithms face challenges such as the cold start problem for new users (also known as the new item problem) and the sparsity issue in matrices. In this study, we focus on a movie recommendation system and transform user ratings for movies into multi-valued modal symbolic data based on user group characteristics. This transformation method takes into account the multiple genres or characteristics that a movie may have, aiming to overcome the cold start problem for new users and reduce the sparsity issue caused by missing values in the matrix. We conducted simulation experiments and analyzed real movie rating data to validate the proposed approach. The results showed that the symbolic data analysis method not only improves recommendation effectiveness but also provides a new approach and method for the development of recommendation systems.
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Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487–499. Santiago, Chile.
Ahuja, R., Solanki, A., and Nayyar, A. (2019). Movie recommender system using k-means clustering and k-nearest neighbor. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 263–268. IEEE.
Basu, C., Hirsh, H., Cohen, W., et al. (1998). Recommendation as classification: Using social and content-based information in recommendation. In Aaai/iaai, pages 714–720.
Bi, X., Qu, A., and Shen, X. (2018). Multilayer tensor factorization with applications to recommender systems. The Annals of Statistics, 46(6B):3308–3333.
Bi, X., Qu, A., Wang, J., and Shen, X. (2017). A group-specific recommender system. Journal of the American Statistical Association, 112(519):1344–1353.
Billard, L. and Diday, E. (2002). Symbolic regression analysis. In Classification, clustering, and data analysis: recent advances and applications, pages 281–288. Springer.
Billard, L. and Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462):470–487.
Billard, L. and Diday, E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Wiley Series in Computational Statistics. Wiley.
Brito, P. (2003). Hierarchical and pyramidal clustering for symbolic data. Journal of the Japanese Society of Computational Statistics, 15:231–244.
Cai, Q. and Tan, W. (2022). Box Office Forecast Model Based on Random Forest and BP Neural Network, page 69–75. Association for Computing Machinery, New York, NY, USA.
de Carvalho, F. d. A. (2007). Fuzzy c-means clustering methods for symbolic interval data. Pattern Recognition Letters, 28(4):423–437.
Deng, F., Ren, P., Qin, Z., Huang, G., and Qin, Z. (2018). Leveraging image visual features in content-based recommender system. Scientific Programming, 2018.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Domingues, M. A., de Souza, R. M., and Cysneiros, F. J. A. (2010). A robust method for linear regression of symbolic interval data. Pattern Recognition Letters, 31(13):1991–1996.
Dutta, S. and Dasgupta, K. (2021). A shallow approach to gradient boosting (xgboosts) for prediction of the box office revenue of a movie. In Mandal, J. K., Mukhopadhyay, S., Unal, A., and Sen, S. K., editors, Proceedings
of International Conference on Innovations in Software Architecture and Computational Systems, pages 207–219, Singapore. Springer Singapore.
Feng, K. and Liu, X. (2020). Adaptive attention with consumer sentinel for movie box office prediction. Complexity, 2020:1–9.
Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., and Karthick, G. (2021). Sentiment analysis on twitter data by using convolutional neural network (cnn) and long short term memory (lstm). Wireless Personal Communications, pages
1–10.
Guo, X., Lin, W., Li, Y., Liu, Z., Yang, L., Zhao, S., and Zhu, Z. (2020). Dken: Deep knowledge-enhanced network for recommender systems. Information Sciences, 540:263–277.
Gupta, B., Prakasam, P., and Velmurugan, T. (2022). Integrated bert embeddings, bilstm-bigru and 1-d cnn model for binary sentiment classification analysis of movie reviews. Multimedia Tools and Applications, 81(23):33067–33086.
Gupta, C., Chawla, G., Rawlley, K., Bisht, K., and Sharma, M. (2021). Senti_alstm: Sentiment analysis of movie reviews using attention-based-lstm. In Abraham, A., Castillo, O., and Virmani, D., editors, Proceedings of 3rd International Conference on Computing Informatics and Networks, pages 211–
219, Singapore. Springer Singapore.
Hoyt, E., Ponto, K., and Roy, C. (2014). Visualizing and analyzing the hollywood screenplay with scripthreads. DHQ: Digital Humanities Quarterly, 8(4).
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Irpino, A. and Verde, R. (2015). Basic statistics for distributional symbolic variables: a new metric-based approach. Advances in Data Analysis and Classification, 9:143–175.
Irpino, A., Verde, R., et al. (2013). Dimension reduction techniques for distributional symbolic data. In Advances in Latent Variables, pages 1–8. Vita e Pensiero.
Iwata, T., Yamada, T., and Ueda, N. (2008). Probabilistic latent semantic visualization: Topic model for visualizing documents. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, KDD ’08, page 363–371, New York, NY, USA. Association for Computing Machinery.
Johnstone, D. J., Barnard, G. A., and Lindley, D. V. (1986). Tests of significance in theory and practice. Journal of the Royal Statistical Society. Series D (The Statistician), 35(5):491–504.
Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J. M., and Heer, J. (2012). Profiler: Integrated statistical analysis and visualization for data quality assessment.
In Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI ’12, page 547–554, New York, NY, USA. Association for Computing Machinery.
Kang, D. (2021). Box-office forecasting in korea using search trend data: a modified generalized bass diffusion model. Electronic Commerce Research, 21(1): 41–72.
Khan, F. H., Qamar, U., and Bashir, S. (2016a). Multi-objective model selection (moms)-based semi-supervised framework for sentiment analysis. Cognitive Computation, 8:614–628.
Khan, F. H., Qamar, U., and Bashir, S. (2016b). Swims: Semi-supervised subjective feature weighting and intelligent model selection for sentiment analysis. Knowledge-Based Systems, 100:97–111.
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描述 碩士
國立政治大學
統計學系
110354026
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110354026
資料類型 thesis
dc.contributor.advisor 吳漢銘zh_TW
dc.contributor.advisor Wu, Han-Mingen_US
dc.contributor.author (Authors) 張順益zh_TW
dc.contributor.author (Authors) CHANG, SHUN-YIen_US
dc.creator (作者) 張順益zh_TW
dc.creator (作者) CHANG, SHUN-YIen_US
dc.date (日期) 2023en_US
dc.date.accessioned 1-Sep-2023 14:57:45 (UTC+8)-
dc.date.available 1-Sep-2023 14:57:45 (UTC+8)-
dc.date.issued (上傳時間) 1-Sep-2023 14:57:45 (UTC+8)-
dc.identifier (Other Identifiers) G0110354026en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146906-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 110354026zh_TW
dc.description.abstract (摘要) 推薦系統（Recommendation System）如今已廣泛應用於商業行銷，涵蓋範疇包括電影、音樂、新聞、書籍、餐廳、3C 商品以及金融服務等產品的推薦。推薦系統能為用戶提供精確的個性化推薦，從而提高商家的營利。協同過濾算法（collaborative filtering）\\citep{Resnick} 是推薦算法中最常見的一種，其根據用戶對商品的評分進行協同過濾，以便找出合適的產品進行推薦。該演算法的理論基礎在於消費行為相近的用戶應該會偏好類似的商品。然而，協同過濾算法面臨新用戶冷啟動（亦稱新商品問題）和稀疏矩陣等問題。在本研究中，我們針對電影推薦系統，根據用戶群的特徵將其對電影的評分依照電影類型轉換成多值模態象徵性資料（multi-valued modal symbolic data）。此轉換方法考慮到每部電影可能具有多種類型的特點，旨在克服新用戶冷啟動問題並減少缺失值導致的稀疏矩陣問題。我們進行了模擬實驗並分析了實際的電影評分資料，以驗證我們提出的新方法。結果顯示，應用象徵性資料分析法不僅可以提升推薦的效果，更為推薦系統的發展開創了一條新的思考途徑和方法。zh_TW
dc.description.abstract (摘要) Recommendation systems are now widely used in business marketing, spanning various domains such as movies, music, news, books, restaurants, 3C products, and financial services. Collaborative filtering, the most common recommendation algorithm, utilizes user ratings on products to perform collaborative filtering and identify suitable items for recommendations. The theoretical basis of this algorithm is that users with similar consumption behaviors are likely to prefer similar items. However, collaborative filtering algorithms face challenges such as the cold start problem for new users (also known as the new item problem) and the sparsity issue in matrices. In this study, we focus on a movie recommendation system and transform user ratings for movies into multi-valued modal symbolic data based on user group characteristics. This transformation method takes into account the multiple genres or characteristics that a movie may have, aiming to overcome the cold start problem for new users and reduce the sparsity issue caused by missing values in the matrix. We conducted simulation experiments and analyzed real movie rating data to validate the proposed approach. The results showed that the symbolic data analysis method not only improves recommendation effectiveness but also provides a new approach and method for the development of recommendation systems.en_US
dc.description.tableofcontents 1 緒論 8
1.1 研究動機 8
1.2 研究目的 9
1.3 文獻回顧 10
1.4 文章結構 16
2 記憶體型協同過濾演算法17
3 多值模態記憶體型協同過濾演算法 19
4 模擬實驗 27
4.1 缺失值比例的影響 27
4.2 Pearson 相關程度高低的影響 28
5 實例分析 29
6 結論與討論 33
參考文獻 34		zh_TW
dc.format.extent 29927223 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110354026en_US
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 象徵性資料分析法zh_TW
dc.subject (關鍵詞) 分群演算法zh_TW
dc.subject (關鍵詞) 遺失值補值zh_TW
dc.subject (關鍵詞) Recommendation Systemen_US
dc.subject (關鍵詞) Symbolic Data Analysisen_US
dc.subject (關鍵詞) Clustering Algorithmen_US
dc.subject (關鍵詞) Missing Value Imputationen_US
dc.title (題名) 應用象徵性資料分析法於電影推薦系統之研究zh_TW
dc.title (題名) The application of symbolic data analysis to movie recommendation systemsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) Abdollahi, B. and Nasraoui, O. (2016). Explainable matrix factorization for collaborative filtering. In Proceedings of the 25th International Conference Companion on World Wide Web, pages 5–6.
Agrawal, R., Srikant, R., et al. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large data bases, VLDB, volume 1215, pages 487–499. Santiago, Chile.
Ahuja, R., Solanki, A., and Nayyar, A. (2019). Movie recommender system using k-means clustering and k-nearest neighbor. In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pages 263–268. IEEE.
Basu, C., Hirsh, H., Cohen, W., et al. (1998). Recommendation as classification: Using social and content-based information in recommendation. In Aaai/iaai, pages 714–720.
Bi, X., Qu, A., and Shen, X. (2018). Multilayer tensor factorization with applications to recommender systems. The Annals of Statistics, 46(6B):3308–3333.
Bi, X., Qu, A., Wang, J., and Shen, X. (2017). A group-specific recommender system. Journal of the American Statistical Association, 112(519):1344–1353.
Billard, L. and Diday, E. (2002). Symbolic regression analysis. In Classification, clustering, and data analysis: recent advances and applications, pages 281–288. Springer.
Billard, L. and Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462):470–487.
Billard, L. and Diday, E. (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Wiley Series in Computational Statistics. Wiley.
Brito, P. (2003). Hierarchical and pyramidal clustering for symbolic data. Journal of the Japanese Society of Computational Statistics, 15:231–244.
Cai, Q. and Tan, W. (2022). Box Office Forecast Model Based on Random Forest and BP Neural Network, page 69–75. Association for Computing Machinery, New York, NY, USA.
de Carvalho, F. d. A. (2007). Fuzzy c-means clustering methods for symbolic interval data. Pattern Recognition Letters, 28(4):423–437.
Deng, F., Ren, P., Qin, Z., Huang, G., and Qin, Z. (2018). Leveraging image visual features in content-based recommender system. Scientific Programming, 2018.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.
Domingues, M. A., de Souza, R. M., and Cysneiros, F. J. A. (2010). A robust method for linear regression of symbolic interval data. Pattern Recognition Letters, 31(13):1991–1996.
Dutta, S. and Dasgupta, K. (2021). A shallow approach to gradient boosting (xgboosts) for prediction of the box office revenue of a movie. In Mandal, J. K., Mukhopadhyay, S., Unal, A., and Sen, S. K., editors, Proceedings
of International Conference on Innovations in Software Architecture and Computational Systems, pages 207–219, Singapore. Springer Singapore.
Feng, K. and Liu, X. (2020). Adaptive attention with consumer sentinel for movie box office prediction. Complexity, 2020:1–9.
Gandhi, U. D., Malarvizhi Kumar, P., Chandra Babu, G., and Karthick, G. (2021). Sentiment analysis on twitter data by using convolutional neural network (cnn) and long short term memory (lstm). Wireless Personal Communications, pages
1–10.
Guo, X., Lin, W., Li, Y., Liu, Z., Yang, L., Zhao, S., and Zhu, Z. (2020). Dken: Deep knowledge-enhanced network for recommender systems. Information Sciences, 540:263–277.
Gupta, B., Prakasam, P., and Velmurugan, T. (2022). Integrated bert embeddings, bilstm-bigru and 1-d cnn model for binary sentiment classification analysis of movie reviews. Multimedia Tools and Applications, 81(23):33067–33086.
Gupta, C., Chawla, G., Rawlley, K., Bisht, K., and Sharma, M. (2021). Senti_alstm: Sentiment analysis of movie reviews using attention-based-lstm. In Abraham, A., Castillo, O., and Virmani, D., editors, Proceedings of 3rd International Conference on Computing Informatics and Networks, pages 211–
219, Singapore. Springer Singapore.
Hoyt, E., Ponto, K., and Roy, C. (2014). Visualizing and analyzing the hollywood screenplay with scripthreads. DHQ: Digital Humanities Quarterly, 8(4).
Irpino, A. and Verde, R. (2006). A new wasserstein based distance for the hierarchical clustering of histogram symbolic data. In Data science and classification, pages 185–192. Springer.
Irpino, A. and Verde, R. (2015). Basic statistics for distributional symbolic variables: a new metric-based approach. Advances in Data Analysis and Classification, 9:143–175.
Irpino, A., Verde, R., et al. (2013). Dimension reduction techniques for distributional symbolic data. In Advances in Latent Variables, pages 1–8. Vita e Pensiero.
Iwata, T., Yamada, T., and Ueda, N. (2008). Probabilistic latent semantic visualization: Topic model for visualizing documents. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data
Mining, KDD ’08, page 363–371, New York, NY, USA. Association for Computing Machinery.
Johnstone, D. J., Barnard, G. A., and Lindley, D. V. (1986). Tests of significance in theory and practice. Journal of the Royal Statistical Society. Series D (The Statistician), 35(5):491–504.
Kandel, S., Parikh, R., Paepcke, A., Hellerstein, J. M., and Heer, J. (2012). Profiler: Integrated statistical analysis and visualization for data quality assessment.
In Proceedings of the International Working Conference on Advanced Visual Interfaces, AVI ’12, page 547–554, New York, NY, USA. Association for Computing Machinery.
Kang, D. (2021). Box-office forecasting in korea using search trend data: a modified generalized bass diffusion model. Electronic Commerce Research, 21(1): 41–72.
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