Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/141038
|
Title: | 基於時序與風格的語音節目推薦系統研究 An investigation of spoken program recommendation systems based on time and style |
Authors: | 蘇品維 Su, Pin-Wei |
Contributors: | 杜雨儒 Tu,Yu-Ju 蘇品維 Su,Pin-Wei |
Keywords: | 推薦系統 冷啟動問題 Podcast 機器學習 時間 敘事風格 : Recommendation systems Cold-start problem Podcast Machine Learning Listening Time Speaking Style |
Date: | 2022 |
Issue Date: | 2022-08-01 17:22:56 (UTC+8) |
Abstract: | 因為網際網路以及串流技術的蓬勃發展,在相關的市場上開始衍伸出許多新媒體服務,例如:影音串流(YouTube) 語音節目(Podcast)。其中又因為語音節目的一些自身的特性,以及在台灣市場當中發生了在短時間內湧入大量新用戶以及新產品的問題,使得在語音節目平台中建置推薦系統顯得相當困難,特別是在推薦新產品或是推薦產品給新用戶的議題上。換句話說,要如何推薦適合的語音節目給新的使用者 或是如何推薦新的語音節目給使用者等議題顯得十分重要且具有挑戰性。這些議題在過去的研究中這些問題也被稱作「冷啟動問題」。 而過去對於語音節目推薦上的相關研究十分稀少。而本研究基於在過去少量的語音節目推薦文獻上所使用之文字描述和類別的特性,結合兩個特有的特性—「時序」與語音節目的「敘事風格」,並搭配混合推薦的方法,去解決在語音節目上所發生的冷啟動問題。本研究也透過網路爬蟲的方式蒐集 APPLE Podcast 的評分資料作為推薦系統的訓練資料。除了線下測試外,也實作出推薦系統介面實際讓使用者進行使用者測試。根據我們的線下測試以及使用者測試之結果可以得知在時序與風格屬性的幫助下,在整體以及新使用者的推薦效果都有顯著的提升。 With the rapid development of the Internet and streaming technology, many new media services have begun to spread to related markets, such as video streaming (YouTube) and spoken program services (podcasts). Due to the characteristics of spoken programs and the influx of new users and products in the Taiwan market in a short time, it is challenging to build a recommendation system in the spoken program platform, especially on the issues of recommending new items or recommending items to new users. In other words, the challenges of recommending suitable spoken programs to new users or recommending new spoken programs to users remain open research questions. These issues are called “cold-start problems” in past studies. Related research on spoken program recommendations was scarce in the past. The present study, based on the characteristics of text descriptions and categories used in a few recommended literatures of spoken programs in the past, combined two unique characteristics—listening time and speaking styles of spoken programs—in a hybrid recommendation method to solve the cold-start problems in spoken programs. The study also collected the rating data of Apple Podcasts through the web crawler, and podcast ratings were used as training data for the recommendation system. In addition to offline testing, we implemented a recommended system interface for users to conduct user testing. According to the results of our offline and user tests, with the addition of time and speaking styles, the performance of the recommendation in overall and new users was significantly improved. |
Reference: | 1. An, H. S. 2017. "Breaking the Pop Music Market in the Us: Effects of Online PrePurchase Influences and Acculturation of Asian Consumers on Their Responses to Asian Artists’ Contemporary Pop Music," Proceedings of the Northeast Business & Economics Association). 2. Arif, I., Aslam, W., and Siddiqui, H. 2020. "Influence of Brand Related UserGenerated Content through Facebook on Consumer Behaviour: A StimulusOrganism-Response Framework," International Journal of Electronic Business (15:2), pp. 109-132. 3. Ayata, D., Yaslan, Y., and Kamasak, M. E. 2018. "Emotion Based Music Recommendation System Using Wearable Physiological Sensors," IEEE transactions on consumer electronics (64:2), pp. 196-203. 4. Bai, T., Wen, J.-R., Zhang, J., and Zhao, W. X. 2017. "A Neural Collaborative Filtering Model with Interaction-Based Neighborhood," Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1979-1982. 5. Balabanović, M., and Shoham, Y. 1997. "Fab: Content-Based, Collaborative Recommendation," Commun. ACM (40:3), pp. 66–72. 6. Beel, J., Langer, S., Genzmehr, M., and Nürnberger, A. 2013. "Introducing Docear`s Research Paper Recommender System," Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries, pp. 459-460. 7. Belk, R. W. 1974. "An Exploratory Assessment of Situational Effects in Buyer Behavior," Journal of marketing research (11:2), pp. 156-163. 8. Benton, G., Fazelnia, G., Wang, A., and Carterette, B. 2020. "Trajectory Based Podcast Recommendation," arXiv preprint arXiv:2009.03859). 9. Bobadilla, J., Ortega, F., Hernando, A., and Bernal, J. 2012. "A Collaborative Filtering Approach to Mitigate the New User Cold Start Problem," Knowledgebased systems (26), pp. 225-238. 10. Burke, R. 2002. "Hybrid Recommender Systems: Survey and Experiments," User modeling and user-adapted interaction (12:4), pp. 331-370. 11. Cai, J.-J., Tang, J., Chen, Q.-G., Hu, Y., Wang, X., and Huang, S.-J. 2019. "MultiView Active Learning for Video Recommendation," IJCAI, pp. 2053-2059. 12. Chen, C.-M., and Sun, Y.-C. 2012. "Assessing the Effects of Different Multimedia Materials on Emotions and Learning Performance for Visual and Verbal Style Learners," Computers & Education (59:4), pp. 1273-1285. 13. Chen, H.-H., Chung, C.-A., Huang, H.-C., and Tsui, W. 2017. "Common Pitfalls in Training and Evaluating Recommender Systems," ACM SIGKDD Explorations Newsletter (19:1), pp. 37-45. 14. Choi, S.-M., Jang, K., Lee, T.-D., Khreishah, A., and Noh, W. 2020. "Alleviating Item-Side Cold-Start Problems in Recommender Systems Using Weak Supervision," IEEE Access (8), pp. 167747-167756. 15. Collins, A., Tkaczyk, D., Aizawa, A., and Beel, J. 2018. "Position Bias in Recommender Systems for Digital Libraries," International Conference on Information: Springer, pp. 335-344. 16. Common-Wealth-Magazine ,2021. Complete investigation report of "2021 Listening to Economic Survey" https://www.cw.com.tw/article/5115003?template=transformers (Last access date 2022/07) 77 17. Darshna, P. 2018. "Music Recommendation Based on Content and Collaborative Approach & Reducing Cold Start Problem," 2018 2nd International Conference on Inventive Systems and Control (ICISC): IEEE, pp. 1033-1037. 18. Das, D., Sahoo, L., and Datta, S. 2017. "A Survey on Recommendation System," International Journal of Computer Applications (160:7). 19. Deng, S., Wang, D., Li, X., and Xu, G. 2015. "Exploring User Emotion in Microblogs for Music Recommendation," Expert Systems with Applications (42:23), pp. 9284-9293. 20. Díaz-Morales, J. F., Escribano, C., and Jankowski, K. S. 2015. "Chronotype and Time-of-Day Effects on Mood During School Day," Chronobiology international (32:1), pp. 37-42. 21. Felício, C. Z., Paixão, K. V., Barcelos, C. A., and Preux, P. 2017. "A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation," Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, pp. 32- 40. 22. Gunawan, A. A., and Suhartono, D. 2019. "Music Recommender System Based on Genre Using Convolutional Recurrent Neural Networks," Procedia Computer Science (157), pp. 99-109. 23. Guo, G., Zhang, J., and Yorke-Smith, N. 2015. "Trustsvd: Collaborative Filtering with Both the Explicit and Implicit Influence of User Trust and of Item Ratings," Proceedings of the AAAI Conference on Artificial Intelligence. 24. He, X., Du, X., Wang, X., Tian, F., Tang, J., and Chua, T.-S. 2018. "Outer ProductBased Neural Collaborative Filtering," arXiv preprint arXiv:1808.03912). 25. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., and Chua, T.-S. 2017. "Neural Collaborative Filtering," Proceedings of the 26th international conference on world wide web, pp. 173-182. 26. Herce-Zelaya, J., Porcel, C., Bernabé-Moreno, J., Tejeda-Lorente, A., and Herrera-Viedma, E. 2020. "New Technique to Alleviate the Cold Start Problem in Recommender Systems Using Information from Social Media and Random Decision Forests," Information Sciences (536), pp. 156-170. 27. Herlocker, J. L., Konstan, J. A., Borchers, A., and Riedl, J. 1999. "An Algorithmic Framework for Performing Collaborative Filtering," Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp. 230-237. 28. Hidalgo, M. P., Caumo, W., Posser, M., Coccaro, S. B., Camozzato, A. L., and Chaves, M. L. F. 2009. "Relationship between Depressive Mood and Chronotype in Healthy Subjects," Psychiatry and clinical neurosciences (63:3), pp. 283-290. 29. Hu, Y., and Ogihara, M. 2011. "Nextone Player: A Music Recommendation System Based on User Behavior," ISMIR, pp. 103-108. 30. Hyung, Z., Lee, K., and Lee, K. 2014. "Music Recommendation Using Text Analysis on Song Requests to Radio Stations," Expert Systems with Applications (41:5), pp. 2608-2618. 31. Inside ,2020. Show you Podcast- 2020 Taiwan Podcast Industry Report for the First Half of the Year https://www.inside.com.tw/article/20391-2020-podcastreport (Last access date 2022/07) 32. Ismailoglu, F. 2021. "Aggregating User Preferences in Group Recommender Systems: A Crowdsourcing Approach," Decision Support Systems), p. 113663. 33. Jelodar, H., Wang, Y., Rabbani, M., Ahmadi, S. B. B., Boukela, L., Zhao, R., and Larik, R. S. A. 2021. "A Nlp Framework Based on Meaningful Latent-Topic 78 Detection and Sentiment Analysis Via Fuzzy Lattice Reasoning on Youtube Comments," Multimedia Tools and Applications (80:3), pp. 4155-4181. 34. Joo, Y. S., and Kim, S.-K. 2020. "Scent Emotion Evaluation Experiment for Multimedia Application," 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia): IEEE, pp. 1-4. 35. Kaji, N., and Kobayashi, H. 2017. "Incremental Skip-Gram Model with Negative Sampling," arXiv preprint arXiv:1704.03956). 36. Kim, H.-N., Ha, I., Lee, K.-S., Jo, G.-S., and El-Saddik, A. 2011. "Collaborative User Modeling for Enhanced Content Filtering in Recommender Systems," Decision Support Systems (51:4), pp. 772-781. 37. Kudielka, B. M., Schommer, N. C., Hellhammer, D. H., and Kirschbaum, C. 2004. "Acute Hpa Axis Responses, Heart Rate, and Mood Changes to Psychosocial Stress (Tsst) in Humans at Different Times of Day," Psychoneuroendocrinology (29:8), pp. 983-992. 38. Kumar, P., and Thakur, R. S. 2018. "Recommendation System Techniques and Related Issues: A Survey," International Journal of Information Technology (10:4), pp. 495-501. 39. Lang, A., and Chrzan, J. 2015. "Media Multitasking: Good, Bad, or Ugly?," Annals of the International Communication Association (39:1), pp. 99-128. 40. Lee, J.-S., and Shin, D.-H. 2015. "A Study on the Preference between Emotion of Human and Media Genre in Smart Device," Science of Emotion and Sensibility (18:1), pp. 59-66. 41. Li, G., and Zhang, J. 2018. "Music Personalized Recommendation System Based on Improved Knn Algorithm," 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC): IEEE, pp. 777-781. 42. Li, Y.-M., Liou, J.-H., and Li, Y.-W. 2020. "A Social Recommendation Approach for Reward-Based Crowdfunding Campaigns," Information & Management (57:7), p. 103246. 43. Liang, T.-P., Lai, H.-J., and Ku, Y.-C. 2006. "Personalized Content Recommendation and User Satisfaction: Theoretical Synthesis and Empirical Findings," Journal of Management Information Systems (23:3), pp. 45-70. 44. Llisterri, J. 1992. "Speaking Styles in Speech Research," Workshop on Integrating Speech and Natural Language: Citeseer. 45. Marshall, D., and Sidorov, K. 2001. "Introduction to Multimedia," UK: School of Computer Science & Informatics Cardiff University). 46. Martikainen, K. 2020. "Audio-Based Stylistic Characteristics of Podcasts for Search and Recommendation: A User and Computational Analysis." University of Twente. 47. Massquantity, 2021. LibRecommender [Source code]. https://github.com/massquantity/LibRecommender. (Last access date 2022/07) 48. Mehrabian, A., and Russell, J. A. 1974. An Approach to Environmental Psychology. the MIT Press. 49. Moscato, V., Picariello, A., and Sperli, G. 2020. "An Emotional Recommender System for Music," IEEE Intelligent Systems). 50. Murray, G., Nicholas, C. L., Kleiman, J., Dwyer, R., Carrington, M. J., Allen, N. B., and Trinder, J. 2009. "Nature’s Clocks and Human Mood: The Circadian System Modulates Reward Motivation," Emotion (9:5), p. 705. 79 51. National Audiovisual Institute, 2022.[Source code] https://github.com/ina-foss/inaSpeechSegmenter (Last access date 2022/07) 52. Nazari, Z., Charbuillet, C., Pages, J., Laurent, M., Charrier, D., Vecchione, B., and Carterette, B. 2020. "Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste," in: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. Virtual Event, China: Association for Computing Machinery, pp. 1041–1050. 53. Panagiotakis, C., Papadakis, H., and Fragopoulou, P. 2021. "A Dual Hybrid Recommender System Based on Scor and the Random Forest," Computer Science and Information Systems (18:1), pp. 115-128. 54. Raheem, K. R., and Ali, I. H. 2020. "Multimodal Content-Based Recommender System Using Three-Dimension Convolution Neural Network," COMPUSOFT: An International Journal of Advanced Computer Technology (9:5). 55. Rana, M. K. C. 2012. "Survey Paper on Recommendation System,"). 56. Rendle, S. 2010. "Factorization Machines," 2010 IEEE International conference on data mining: IEEE, pp. 995-1000. 57. Roenneberg, T. 2012. "What Is Chronotype?," Sleep and biological rhythms (10:2), pp. 75-76. 58. Ruff, J. 2002. "Information Overload: Causes, Symptoms and Solutions," Harvard Graduate School of Education), pp. 1-13. 59. Ryu, J., Capistrano, E. P., and Lin, H.-C. 2020. "Non-Korean Consumers’ Preferences on Korean Popular Music: A Two-Country Study," International Journal of Market Research (62:2), pp. 234-252. 60. Sanz-Cruzado, J., Castells, P., and López, E. 2019. "A Simple Multi-Armed Nearest-Neighbor Bandit for Interactive Recommendation," Proceedings of the 13th ACM Conference on Recommender Systems, pp. 358-362. 61. Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. 2000. "Application of Dimensionality Reduction in Recommender System-a Case Study," Minnesota Univ Minneapolis Dept of Computer Science. 62. Selvi, C., and Sivasankar, E. 2019. "An Efficient Context-Aware Music Recommendation Based on Emotion and Time Context," in Data Science and Big Data Analytics. Springer, pp. 215-228. 63. Shani, G., and Gunawardana, A. 2011. "Evaluating Recommendation Systems," in Recommender Systems Handbook. Springer, pp. 257-297. 64. Son, J., and Kim, S. B. 2018. "Academic Paper Recommender System Using Multilevel Simultaneous Citation Networks," Decision Support Systems (105), pp. 24-33. 65. Subramaniyaswamy, V., and Logesh, R. 2017. "Adaptive Knn Based Recommender System through Mining of User Preferences," Wireless Personal Communications (97:2), pp. 2229-2247. 66. Sulaiman, N., Muhammad, A. M., Ganapathy, N. N. D. F., Khairuddin, Z., and Othman, S. 2017. "A Comparison of Students` Performances Using Audio Only and Video Media Methods," English language teaching (10:7), pp. 210-215. 67. Taylor, B. J., and Hasler, B. P. 2018. "Chronotype and Mental Health: Recent Advances," Current psychiatry reports (20:8), pp. 1-10. 68. Thales,2021. PySpeech [Source code] https://github.com/thalesaguiar21/PySpeech (Last access date 2022/07) 69. Thorat, P. B., Goudar, R., and Barve, S. 2015. "Survey on Collaborative Filtering, Content-Based Filtering and Hybrid Recommendation System," International Journal of Computer Applications (110:4), pp. 31-36. 80 70. Tsagkias, M., Larson, M., and De Rijke, M. 2010. "Predicting Podcast Preference: An Analysis Framework and Its Application," Journal of the American Society for information Science and Technology (61:2), pp. 374-391. 71. Volkovs, M., Yu, G. W., and Poutanen, T. 2017. "Content-Based Neighbor Models for Cold Start in Recommender Systems," in Proceedings of the Recommender Systems Challenge 2017. pp. 1-6. 72. Volokhin, S., and Agichtein, E. 2018. "Understanding Music Listening Intents During Daily Activities with Implications for Contextual Music Recommendation," Proceedings of the 2018 Conference on Human Information Interaction & Retrieval, pp. 313-316. 73. Wang, D., Zhang, X., Yu, D., Xu, G., and Deng, S. 2020a. "Came: Content-and Context-Aware Music Embedding for Recommendation," IEEE transactions on neural networks and learning systems (32:3), pp. 1375-1388. 74. Wang, R., Ma, X., Jiang, C., Ye, Y., and Zhang, Y. 2020b. "Heterogeneous Information Network-Based Music Recommendation System in Mobile Networks," Computer Communications (150), pp. 429-437. 75. Wang, X., Rosenblum, D., and Wang, Y. 2012. "Context-Aware Mobile Music Recommendation for Daily Activities," Proceedings of the 20th ACM international conference on Multimedia, pp. 99-108. 76. Xing, Z., Parandehgheibi, M., Xiao, F., Kulkarni, N., and Pouliot, C. 2016. "Content-Based Recommendation for Podcast Audio-Items Using Natural Language Processing Techniques," 2016 IEEE International Conference on Big Data (Big Data): IEEE, pp. 2378-2383. 77. Xu, L., Wen, X., Shi, J., Li, S., Xiao, Y., Wan, Q., and Qian, X. 2021. "Effects of Individual Factors on Perceived Emotion and Felt Emotion of Music: Based on Machine Learning Methods," Psychology of Music (49:5), pp. 1069-1087. 78. Xu, X., Dutta, K., and Ge, C. 2018. "Do Adjective Features from User Reviews Address Sparsity and Transparency in Recommender Systems?," Electronic Commerce Research and Applications (29), pp. 113-123. 79. Yang, L., Sobolev, M., Wang, Y., Chen, J., Dunne, D., Tsangouri, C., Dell, N., Naaman, M., and Estrin, D. 2019. "How Intention Informed Recommendations Modulate Choices: A Field Study of Spoken Word Content," The World Wide Web Conference, pp. 2169-2180. 80. Ye, B. K., Tu, Y. J. T., and Liang, T. P. 2019. "A Hybrid System for Personalized Content Recommendation," Journal of Electronic Commerce Research (20:2), pp. 91-104. 81. Yin, H., Cui, B., Li, J., Yao, J., and Chen, C. 2012. "Challenging the Long Tail Recommendation," arXiv preprint arXiv:1205.6700). 82. Yu, T., Guo, J., Li, W., and Lu, M. 2021. "A Mixed Heterogeneous Factorization Model for Non-Overlapping Cross-Domain Recommendation," Decision Support Systems), p. 113625. 83. Zhang, H., Lu, Y., Gupta, S., and Zhao, L. 2014. "What Motivates Customers to Participate in Social Commerce? The Impact of Technological Environments and Virtual Customer Experiences," Information & Management (51:8), pp. 1017- 1030. 84. Zhu, Y., Lin, J., He, S., Wang, B., Guan, Z., Liu, H., and Cai, D. 2019. "Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning," IEEE Transactions on Knowledge and Data Engineering (32:4), pp. 631-644 |
Description: | 碩士 國立政治大學 資訊管理學系 109356024 |
Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0109356024 |
Data Type: | thesis |
DOI: | 10.6814/NCCU202201103 |
Appears in Collections: | [資訊管理學系] 學位論文
|
Files in This Item:
File |
Description |
Size | Format | |
602401.pdf | | 3014Kb | Adobe PDF2 | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|