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研究生:歐力文
研究生(外文):Li-Wen Ou
論文名稱:以混和協同過濾法為基礎之個人化推薦服裝
論文名稱(外文):Personalized Outfit Recommendations Based on Hybrid Collaborative Filtering Model
指導教授:江佩穎江佩穎引用關係
口試委員:紀明德謝東儒江佩穎
口試日期:2016-07-14
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:服裝風格模型貝氏網路模型服裝推薦協同過濾與推薦系統
外文關鍵詞:Model Selection and Structure LearningRecommended ApparelBayesian network modelCollaborative Filtering and Recommender Systems
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本論文研究目的是要幫助使用者找到符合個人風格喜好的推薦服裝,本系統首先參考貝氏網路演算法,幫助分析服裝網站的資料,藉此訓練許多不同風格服裝組合,之後就可自動分析使用者擁有的服裝的款式、顏色、部位等屬性與不同風格的相似性,列出數種風格差異較大的服裝組合,給予使用者評分,藉由協同過濾演算法推演使用者對於相似服裝的喜好,同時也分析其他相似喜好使用者的風格,找出最能代表使用者喜好的服裝風格,最後產生的推薦服裝藉由NDCG指標評分比較,相較於其他方法,所產生的結果較符合使用者的服裝喜好。
Our research is intended to help users find the personal style preferences in line with the recommendation of Apparel. We first analyze the outfits website information on the use of Bayesian network algorithms, training many different styles Apparel combinations, then you can automatically analyze the user’s cloth outfit with style, color, location and other attributes, and compared with the different styles of similarity. Produce several styles quite different outfit combinations, giving the user ratings. With our collaborative filtering algorithms based on users for similar outfit style preferences, to analyze the preferences of other users of a similar style, find the most representative style of clothing, and finally recommended outfit style. Then we use NDCG, the Normalized Discounted Cumulative Gain, which is a family of ranking measures widely used in practice, to compare the resulting recommendation outfit style, compared to the results of other methods produced more in line with the users outfit style preferences.
摘 要 i
Abstract ii
致 謝 iii
目 錄 iv
圖目錄 vi
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目標 3
1.3 論文結構 4
1.4 論文貢獻 4
第二章 相關文獻與技術 5
2.1 相關文獻 5
2.1.1 辨識衣服紋理、材質特徵 5
2.1.2 人體服裝辨識. 6
2.1.3 服裝穿著配對 8
2.1.4 喜好推薦系統 9
2.2 參考技術 11
2.2.1 LBP 紋理分析 11
2.2.2 顏色相似度計算 12
2.2.3 服裝相似度計算 12
2.2.4 貝氏定理與貝氏分類 14
2.2.5 混和式協同過濾 16
2.2.6 NDCG評量指標計算 19
第三章 系統概觀 19
3.1 系統架構與特色 20
3.1.1 系統初始化 20
3.1.2 產生推薦服裝 20
3.1.1 系統流程 20
第四章 系統實作方法 22
4.1 建立服裝屬性 22
4.2 風格相似度模型 23
4.2.1 風格代表性服裝 23
4.2.1 貝氏網路模型 24
4.2.2 風格相似度計算 24
4.3 混合式協同過濾演算法 25
4.4 風格相似度模型與混和式協同過濾演算法 25
第五章 實驗與比較 27
5.1 實驗目的 27
5.1.1 實驗設置 27
5.1.2 受測人員 27
5.1.3 NDCG評價指標與MAE評價指標 27
5.2 實驗A 28
5.2.1 步驟一 29
5.2.2 步驟二 29
5.3 實驗B 29
5.3.1 步驟一 29
5.3.2 步驟二 29
5.4 其他算法 30
5.5 比較結果 30
5.6 系統成果展示 31
第六章 結論與未來展望 33
6.1 結論 33
6.2 系統限制 33
6.3 未來展望 34
參考文獻 36
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12.Huang, H. S. and Hsu, C. N., "Smoothing of Recommenders Ratings for Collaborative Filtering," Proceedings of the TAAI Conference on Artificial Intelligence and Applications, 2001.
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21.Yu, Lap-Fai., Yeung, S. K., Terzopoulos, D., & Chan, T. F, "DressUp!: outfit synthesis through automatic optimization," ACM Trans. Graph. 31.6 : 134, 2012.
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