結合人格特質與海報主色以類神經網路推薦電影之研究

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姓名

李逸群(I-Chun Lee) 查詢紙本館藏

畢業系所

企業管理學系在職專班

論文名稱

結合人格特質與海報主色以類神經網路推薦電影之研究
(Utilize personality traits and poster colors to recommend movies with neural networks)

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摘要(中)

本研究旨在探討結合人格特質與海報主色於類神經網路推薦電影之效果。我們使用了Moivelens資料庫的用戶大五人格特質資料，從IMDb網站上蒐集電影海報資料，透過影像處理技術提取出海報主要色彩，再透過多層感知器回歸分析，以推薦符合用戶人格特質與喜好的電影。
實驗結果顯示，將電影評分、人格特質和海報主色結合後，推薦系統的準確性有所提升。回歸分析的結果顯示，此三種資料的解釋力達34.2%，RMSE為0.816，MSE為0.666。
綜合以上結果，本研究提出了一個結合電影評分、人格特質和海報主色的推薦系統，可透過多層感知器回歸分析提供更精確的推薦服務。此推薦系統可為電影愛好者提供更加個性化和符合自身喜好的電影推薦。

摘要(英)

This study aims to explore the effectiveness of combining personality traits and poster dominant colors in neural network-based movie recommendations. We used the Big Five personality traits test data from the MovieLens database and collected movie poster data from the IMDb website. Through image processing techniques, we extracted the dominant colors of the posters and used a multi-layer perceptron regression analysis to recommend movies that match user′s personality traits and preferences.
The experimental results showed that the accuracy of the recommendation system improved when combining movie ratings, personality traits, and poster dominant colors. The regression analysis results showed that the explanatory power of these three types of data reached 34.2%, with an RMSE of 0.816 and MSE of 0.666.
Based on these results, this study proposes a recommendation system that combines movie ratings, personality traits, and poster dominant colors, which can provide more accurate recommendation services through multi-layer perceptron regression analysis. This recommendation system can provide movie enthusiasts with more personalized and tailored movie recommendations that align with their preferences.

關鍵字(中)

★ 人格特質
★ 海報主色
★ 多層感知器
★ 推薦系統
★ 電影推薦

關鍵字(英)

★ personality traits
★ poster colors
★ multi-layer perceptron
★ recommendation system
★ movie recommendations

論文目次

中文摘要 ii
Abstract iii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 viii
一、緒論 1
1-1 研究背景 1
1-2 研究動機 1
1-3 研究目的 2
1-4 研究架構 2
二、文獻探討 4
2-1 人格特質 4
2-2 主色概念 7
2-3 電影喜好與大五人格 9
2-4 電影喜好與電影海報 10
2-5 相似度 11
2-6 推薦系統 13
2-7 基於記憶的協同過濾（Memory-based CF） 14
2-7-1 基於用戶的協同過濾（User-based CF） 14
2-7-2 基於物品的協同過濾（Item-based CF） 16
2-8 基於模型的協同過濾（Model-based CF） 17
2-9 多層感知器（Multi-layer Perceptron） 19
三、研究方法 21
3-1 數據來源 23
3-2 電影海報收集 24
3-3 海報主色分析 25
3-4 用戶喜好顏色分析 27
3-5 相似度測量 28
3-6 預測初始評分 29
3-7 用戶/電影評分之平均值計算 29
3-8 多層感知器計算 30
四、實驗結果 33
4-1 自變數 33
4-2 隱藏層元素數量分析 33
4-3 最大迭代次數分析 36
4-4 solver差異分析 38
4-5 activation差異分析 38
4-6 alpha差異分析 41
4-7 隱藏層層數分析 42
4-8 batch size差異比較 46
4-9 顏色、大五人格、電影評分與多層感知器預測比較 50
五、結論、建議與未來方向 52
參考文獻 55

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指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2023-7-18

推文