研究生: |
李銘瑜 Ming-Yu Lee |
---|---|
論文名稱: |
運用專家資訊於使用者偏好估計以減輕個人化服務之稀疏問題 Associating Expertized Information on Preference Estimation for Alleviating Sparsity Problem in Personalization |
指導教授: |
李漢銘
Hahn-Ming Lee |
口試委員: |
何正信
Cheng-Seen Ho 鮑興國 Hsing-Kuo Pao 何建明 Jan-Ming Ho 黃淇竣 Chi-Chun Huang |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 資訊工程系 Department of Computer Science and Information Engineering |
論文出版年: | 2005 |
畢業學年度: | 93 |
語文別: | 英文 |
論文頁數: | 91 |
中文關鍵詞: | 推薦系統 、協同式過濾 、個人化 、專家資訊 |
外文關鍵詞: | Sparsity Problem |
相關次數: | 點閱:234 下載:0 |
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線上資訊的急遽成長促進了個人化服務與推薦系統的發展。為了切合使用者不同的需求以減輕資訊負載的問題,推薦技術被相繼地提出以提供使用者更適切的服務。在眾多被提出的推薦技術中,以項目為基礎的協同過濾推薦方法(item-based collaborative filtering approach)是最成功且最常被採用的推薦技術。然而,這種方法卻會因為使用者偏好評價(preference rating)的缺乏而降低推薦品質,此外,當新進的使用者未給予足夠的喜好評價之前,此種方法卻缺乏對其做推薦的能力,此即個人化服務之稀疏問題。
在此篇論文中,我們提出了一種新的推薦技術用以解決上述的稀疏問題。我們藉由整合圖書資訊專家的階層化分類知識來加強書本之間的相似度計算,接著利用其與使用者偏好的關聯度對偏好評價表(preference rating table)的遺失值(missing value)做估算,最後以估算後的評價表作為協同過濾推薦方法的輸入評價表,以改善偏好稀疏的問題。藉由實驗的分析結果,證明我們所提出的推薦技術確實提升了推薦的數量和品質,改善了因稀疏的使用者偏好評價所造成的問題。
Exponential growth of data on the Web has stimulated the development of personalization. Among different recommendation technologies in personalization, item-based collaborative filtering approach is the most successful and widely adopted one to date. Despite the effectiveness, it still suffers from the problem of sparse preference ratings and lacks the ability to provide recommendations for new items and users. Item-based collaborative filtering approach recommends items based on users’ preference ratings and similarities between items derived from the preference rating table. That is, if the preference ratings are insufficient, the similarity calculation will be unreliable and deteriorate the recommendation quality.
In this study, we devise a methodology in recommender systems designed to alleviate the sparsity and cold-start problems. The expertized hierarchical classification information in Library Science is introduced and associated to enhance the similarity computation between books. The enhanced similarities and preference ratings are used to estimate the missing values of preference rating table. Then by feature-augmentation hybridization technique, the estimated preference rating table is taken as input to the item-based collaborative filtering approach to make recommendations. To prove the performance, our evaluation is conducted offline on existing data set. From the experimental results, our proposed recommendation system outperforms the classic item-based collaborative filtering approach in both recommendation quantities and qualities.
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