預測冷啟動的新片熱門度

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姓名

張嘉玲(Chia-Ling Chang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

預測冷啟動的新片熱門度
(Predicting the popularity of new video for cold start problem.)

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摘要(中)

隨著網路的普及，人們在網路上分享影片並觀看視頻，已成為人們每天都在做的事情，然而，如何在眾多影片中找到熱門的影片，成為影片管理者、廣告商、影片製造商所關心的事。過去研究在預測熱門影片，依賴過去的歷史資料來作預測，當無歷史資料時，將會面臨傳統推薦上的冷啟動問題。本研究以剛上傳的影片作為預測，找出三十六個預測變數建模，並以五個機器學習的分類演算法(類神經、貝式、支持向量機、羅吉斯回歸、決策樹)作為集成學習ENSEMBLE方法，來建立一至十週的預測模型。本研究另外針對各個屬性構面作為挑選，並探討影響影片熱門度的關鍵屬性。本研究結果顯示，本研究的預測模型，均有很好的預測能力，並且可以解決新影片上傳的冷啟動問題。

摘要(英)

Predicting video popularity is an important task involved in managing video-sharing sites. Although many previous studies have investigated this problem, a weakness common to these studies is that their predictions rely on video access data from the past. In other words, they cannot predict the popularity of newly uploaded videos. To handle this cold start problem, this study focused on building prediction models that use only the data available at the time when a video is initially uploaded. Through supervised learning methods, this study employed prediction models to predict the popularity of videos. To further improve the overall accuracy of the prediction, we used an ensemble model to integrate these classification results to obtain the most accurate prediction. The empirical evaluation indicated that the models are effective for predicting the popularity of a video and that our model can solve the cold start problem of video popularity prediction.

關鍵字(中)

★ 資料探勘
★ 熱門度預測
★ 冷啟動
★ YouTube

關鍵字(英)

★ data mining
★ popularity prediction
★ cold start
★ YouTube

論文目次

摘要 I
Abstract II
Table of contents III
List of Figures IV
List of Tables VI
1. Introduction 1
2. Background and Related work 4
2.1 YouTube 4
2.2 Predicting the popularity of video 5
3. Problem description 9
3.1 Popularity of a video 9
3.2 Predictive variables 9
3.2.1 Relevance videos perspective (RV) 11
3.2.2 Author perspective (AU) 12
3.2.3 Keyword perspective (KW) 13
3.3 Similarity computation by title(TL), tag(TG), description(DS) 14
3.4 Ensemble Model 19
3.5 Threshold metrics for performance evaluation 21
4. Evaluation 23
4.1 Study1: Using the all variables to predict popularity. 26
4.2 Study2: Using specific features to predict popularity. 29
4.2.1 Using the relevance videos (RV) category. (Variables 1-12) 29
4.2.2 Using the author (AU) category. (Variables 13-24) 33
4.2.3 Using the keyword (KW) category. (Variables 25-36) 36
4.2.4. Using the title (TL) category. 39
4.2.5. Using the tag (TG) category. 42
4.2.6. Using the description (DS) category. 45
4.2.7 Using the view count (VC) category. 48
4.2.8 Using the preference (PR) category. 51
4.2.9 Using the subscriptions (SB) category. 54
4.2.10. Using the comment count (CM) features. 57
4.3 Study3: Variables selection 60
4.4 Study4: Evaluation of the ensembles in TOP3-TOP9. 64
5. Discussion and conclusion 66
References 70

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指導教授

陳彥良(Yen-Liang Chen)

審核日期

2018-7-13

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