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系統識別號 U0026-1308201915251500
論文名稱(中文) 使用關係與情境特徵進行社群文章電影預告推薦
論文名稱(英文) Using Relationship and Scenario Features of Plot Summaries for Social Article Trailer Recommendation
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 簡君聿
研究生(英文) Chun-Yu Chien
電子信箱 P76064538@mail.ncku.edu.tw
學號 P76064538
學位類別 碩士
語文別 英文
論文頁數 53頁
口試委員 指導教授-盧文祥
口試委員-吳世弘
口試委員-梁勝富
口試委員-楊中平
中文關鍵字 預告片推薦  情節摘要分析  文章分析  Word2Vec  變壓器雙向編碼器表示  卷積神經網絡  支持向量機  隨機森林分類器 
英文關鍵字 Trailer Recommendation  Plot summaries Analysis  Article Analysis  Word2Vec  Bidirectional Encoder Representations from Transformers  Convolutional Neural Network  Support Vector Machine  Random Forest Classifier 
學科別分類
中文摘要 社交平台上發表文章是年輕人最喜歡的活動。隨著電影產業的潛力,開發自動電影推薦引擎成為一個熱門話題。在社交媒體上,在共享相關預告片與關於日常生活在線社交平台的用戶生成文章的場景中,用戶傾向於選擇考慮其抒情主題的預告片。

為了解決上述問題,我們提出了一種基於關係 - 場景的預告片推薦系統,該系統可以通過分析抒情主題來推薦預告片列表到輸入文章。我們認為抒情主題是關係和情景的結合,是情節總結的主觀和客觀視角。通過利用關係情景數據庫(Extend-HowNet 作為知識庫),我們提取情節摘要和文章的關係和情景特徵。關係特徵表示為人物,情感,事件,地點和時間實體的實現。場景特徵表示為情感和事件實體的實現。

因此,我們使用關係和場景特徵提供更好的推薦結果,而不僅僅考慮其中一個特徵,最後我們的推薦系統在用戶偏好和系統性能的兩個實驗中都優於新的技術(W2V)。並且考慮了系統推出不同關係情境之預告片做評估。
英文摘要 The post articles on the social platform is the favorite activity of young people. With the potential of digital movie industry, developing automatic movie recommendation engines becomes a popular issue. On social media, in the
scenario of sharing related trailers with user-generated articles about daily life online social platforms, users tend to choose trailers considering their lyrical theme.

To solve the above problem, we present a Relationship-Scenario-based Trailer Recommendation System which can recommend list of trailers to an input article by analyzing lyrical theme. We consider lyrical theme as a combination of Relationship and Scenario, the subjective and objective perspective of plot summaries. By utilizing relationship-scenario Database (Extended-HowNet as Knowledge base), we extract relationship and scenario features of plot summaries and articles. Relationship feature is represented as character, emotion, event, location and time entity relation. And scenario feature is represented as emotion and event entity relation.

Consequently, we show that using both relationship and scenario features provide better recommendation results than merely consider one of the features, In the end our recommender system outperforms a novel W2V baseline in both experiments of user preference and system performance. Also we consider user preference on our system about different relationship class.
論文目次 摘要 III
Abstract IV
致謝 VI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Method 4
1.4 Contribution 6
1.5 Organization of this Dissertation 6
Chapter 2 Related Work 7
2.1 Studies on Sentiment analysis 7
2.2 Studies on Film Trailer Topic Detection based on Plot Summaries 7
2.3 Studies on Film Trailer Recommendation 8
Chapter 3 Method 10
3.1 System Framework 10
3.2 Preliminaries 12
3.2.1 CKIP Parser 12
3.2.2 Extended-HowNet 13
3.2.3 Data Sets and Preprocessing Steps 14
3.3 Feature Generation 16
3.3.1 Named Entity Recognition 16
3.3.2 Training 20
3.3.3 Relationship & Scenario Database 23
3.4 Relationship Classification 26
3.4.1 Relationship Term Extracting 27
3.4.2 Relationship Model Training 28
3.5 Scenario Classification 30
3.5.1 Scenario Model Training 31
3.6 Scenario Analysis 33
3.7 Relationship-Scenario based Recommendation 36
Chapter 4 Experiments 37
4.1 Dataset 37
4.2 Experiment of Entity Embedding Quality 38
4.2.1 Dataset for Word Embedding 38
4.2.2 Evaluation Metrics 38
4.2.3 Evaluation Result 39
4.3 Experiment of Relationship Classification 41
4.3.1 Dataset for Relationship Classification 41
4.3.2 Evaluation Metrics 43
4.3.3 Experiment Result 44
4.4 Experiment of Scenario Classification 44
4.4.1 Dataset for Scenario Classification 44
4.4.2 Evaluation Metrics 46
4.4.3 Experiment Result 46
4.5 Evaluation of Relationship-Scenario based Trailer Recommendation 47
4.5.1 Evaluation Set and Evaluate User Preference Setting 47
4.5.2 Experiment Result 50
Chapter 5 Conclusions 51
Reference 52
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