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題名 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
作者 蘇品維
Su, Pin-Wei
貢獻者 杜雨儒
Tu,Yu-Ju
蘇品維
Su,Pin-Wei
關鍵詞 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
日期 2022
上傳時間 1-Aug-2022 17:22:56 (UTC+8)
摘要 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.
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描述 碩士
國立政治大學
資訊管理學系
109356024
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109356024
資料類型 thesis
dc.contributor.advisor 杜雨儒zh_TW
dc.contributor.advisor Tu,Yu-Juen_US
dc.contributor.author (Authors) 蘇品維zh_TW
dc.contributor.author (Authors) Su,Pin-Weien_US
dc.creator (作者) 蘇品維zh_TW
dc.creator (作者) Su, Pin-Weien_US
dc.date (日期) 2022en_US
dc.date.accessioned 1-Aug-2022 17:22:56 (UTC+8)-
dc.date.available 1-Aug-2022 17:22:56 (UTC+8)-
dc.date.issued (上傳時間) 1-Aug-2022 17:22:56 (UTC+8)-
dc.identifier (Other Identifiers) G0109356024en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141038-
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) 109356024zh_TW
dc.description.abstract (摘要) 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。		zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents Table of Contents
CHAPTER 1: Introduction 6
1-1 Research motivation 6
CHAPTER2: Literature Review 10
2-1 Methods in recommendation systems 10
2-2 New user and new items recommendations 13
2-3 Related works in hybrid multimedia recommendation systems 14
2-3-1 Hybrid method in multimedia recommendations 14
2-3-2 The features of user and item in multimedia recommendations 22
2-4 The features of spoken program recommendations 24
2-4-1 Text-based recommendation 24
2-4-2 Speaking style 27
2-4-3 Listening Time 28
CHAPTER3: Proposed Model 31
CHAPTER4: Empirical Experiments 39
4-1 Baselines 39
4-2 Experimental design 39
4-2-1 The offline test approach 40
4-2-2 The user test approach 47
CHAPTER 5: Summary of Findings and Discussion 55
5-1 The findings in offline test 55
5-2 The findings in user test 61
5-2-1 Data description of the user test samples 61
5-2-2 The findings in user test 63
5-3 Discussion 70
CHAPTER6: Conclusion 74
References 76
Appendix 81		zh_TW
dc.format.extent 3087016 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109356024en_US
dc.subject (關鍵詞) 推薦系統zh_TW
dc.subject (關鍵詞) 冷啟動問題zh_TW
dc.subject (關鍵詞) Podcastzh_TW
dc.subject (關鍵詞) 機器學習zh_TW
dc.subject (關鍵詞) 時間zh_TW
dc.subject (關鍵詞) 敘事風格zh_TW
dc.subject (關鍵詞) : Recommendation systemsen_US
dc.subject (關鍵詞) Cold-start problemen_US
dc.subject (關鍵詞) Podcasten_US
dc.subject (關鍵詞) Machine Learningen_US
dc.subject (關鍵詞) Listening Timeen_US
dc.subject (關鍵詞) Speaking Styleen_US
dc.title (題名) 基於時序與風格的語音節目推薦系統研究zh_TW
dc.title (題名) An investigation of spoken program recommendation systems based on time and styleen_US
dc.type (資料類型) thesisen_US
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dc.identifier.doi (DOI) 10.6814/NCCU202201103en_US