使用擴充資料進行共分群的協同式推薦系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：13

、訪客IP：3.146.152.33

姓名

劉睿哲(Rui-Zhe Liu) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

使用擴充資料進行共分群的協同式推薦系統
(Collaborative Filtering based on Co-clustering with CCAM)

相關論文

★ 行程邀約郵件的辨識與不規則時間擷取之研究	★ NCUFree校園無線網路平台設計及應用服務開發
★ 網際網路半結構性資料擷取系統之設計與實作	★ 非簡單瀏覽路徑之探勘與應用
★ 遞增資料關聯式規則探勘之改進	★ 應用卡方獨立性檢定於關連式分類問題
★ 中文資料擷取系統之設計與研究	★ 非數值型資料視覺化與兼具主客觀的分群
★ 關聯性字組在文件摘要上的探討	★ 淨化網頁：網頁區塊化以及資料區域擷取
★ 問題答覆系統使用語句分類排序方式之設計與研究	★ 時序資料庫中緊密頻繁連續事件型樣之有效探勘
★ 星狀座標之軸排列於群聚視覺化之應用	★ 由瀏覽歷程自動產生網頁抓取程式之研究
★ 動態網頁之樣版與資料分析研究	★ 同性質網頁資料整合之自動化研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

近二十年來，推薦系統已成為一門廣受歡迎的研究領域，無論是學術界或者是業界都投以高度的興趣。身為推薦系統的其中一個分支，協同式過濾推薦系統尤其以藉由分享同儕的意見而能夠高度精確的預測使用者喜好而經常受到學者專家的討論。然而，協同式過濾推薦系統仍有它所需要面對的問題，最常見的就是缺乏已知資料的議題。相對於大量的未知資料，通常資料集會嚴重缺乏可加以利用的已知的資料，因此預測使用者的喜好所需的資訊將會非常稀少。
在這篇論文中，我們引用一種新奇的協同式過濾推薦系統，同時結合基於使用者的喜好進行推薦的演算法、基於廣告的受歡迎程度進行推薦的演算法以及考慮擴充矩陣的共分群演算法 - 一種在透過最佳化共分群以減少因共分群而損失的資訊時又能夠從多個相關矩陣中考量有用的資訊而能夠運算出更佳的分群效果的演算法以產生最終的推薦。在我們提出的混合模型實驗結果中，它在預測使用者喜好的誤差相對於相關的演算法例如k-Means、k-NN以及ITCC表現的更為出色，顯示出在缺乏已知資料的議題上能藉由同時應用多重矩陣之間的資訊而能夠產生更佳的處理結果。

摘要(英)

Recommender system has become an important research area since the high interest of academia and industry. As a branch of recommender systems, collaborative filtering systems take its roots from sharing opinions with others and have been shown to be very effective for generating high quality recommendations. However, CF often confronts a problem of sparsity which is caused by relevantly less number of ratings against the unknowns that need to be predicted.
In this paper, we consider a hybrid approach which combines the content-based approach with collaborative filtering under a unified model called co-clustering with augmented data matrix (CCAM) that overrides information-theoretic co-clustering (ITCC) in order to further consider augmented data like user profile and item description. By presenting results on a better error of prediction, we show that when model-based CF method was integrated with memory-based method, our algorithm is the more effective than state-of-the-art algorithms k-NN, k-Means and ITCC through optimizing the co-cluster in mutual information loss between multiple tabular data in addressing sparsity.

關鍵字(中)

★ 分群
★ 協同式推薦
★ 資訊理論
★ 額外資料
★ 共分群
★ 推薦系統
★ 文本式推薦
★ 廣告

關鍵字(英)

★ information theory
★ clustering
★ recommender system
★ content-based filtering
★ collaborative filtering
★ ad
★ co-clustering
★ coclustering
★ extensive data
★ augmented

論文目次

摘要
Abstract
誌謝
Table of Contents
List of Figures
List of Tables
Chapter 1. Introduction 1
Chapter 2. Related Work 4
Chapter 3. Preliminary 6
Chapter 4. Collaborative Filtering with CCAM 8
4.1 Co-Clustering with Augmented data Matrix 8
4.2 Collaborative Filtering with CCAM 16
Chapter 5. Experiments 20
5.1 Data Sets 20
5.2 Evaluation methodology 22
5.3 Results 23
Chapter 6. Conclusion and Future Work 30
Reference 32

參考文獻

[1] Gediminas Adomavicius, and Alexander Tuzhilin. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, (pp. 734-749).
[2] Arindam Banerjee, Inderjit Dhillon, Joydeep Ghosh, Srujana Merugu, and Dharmendra S. Modha. (2004). A generalized maximum entropy approach to bregman co-clustering and matrix approximation. In KDD'04: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (pp. 509-514).
[3] Gang Chen, Fei Wang, and Changshui Zhang. (2007). Collaborative filtering using orthogonal nonnegative matrix tri-factorization. Inf. Process. Manage, Vol. 45, Pergamon Press, Inc., Tarrytown, NY, USA, (pp. 368-379).
[4] Thomas M. Cover, and Joy A. Thomas. (2006). Elements of information theory. 2nd Edition, New York, Wiley-Interscience.
[5] Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. (2007). Co-clustering based classification for out-of-domain documents. In KDD'07: Proceedings of the 13th ACM SIGKDD International conference on Knowledge Discovery and Data Mining, ACM, New York, NY, USA, (pp. 210V-219).
[6] Inderjit S. Dhillon, Subramanyam Mallela, and Dharmendra S. Modha. (2003). Information theoretic co-clustering., In KDD'03: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2003. ACM Press, (pp. 89-98).
[7] Chris Ding, Tao Li, Wei Peng, and Haesun Park. (2006). Orthogonal nonnegative matrix tri-factorizations for clustering. In KDD06: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA. ACM Press, (pp. 126V-135).
[8] Thomas George, and Srujana Merugu. (2005). A scalable collaborative filtering framework based on co-clustering. In ICDM 05: Proceedings of the 5th IEEE International Conference on Data Mining, Washington, DC, USA, IEEE Computer Society, (pp. 625V-628).
[9] Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, John, and T. Riedl. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, Vol. 22, (pp. 5-53).
[10] Bin Li, Qiang Yang, and Xiangyang Xue. (2009). Can movies and books collaborate?: cross-domain collaborative filtering for sparsity reduction. In Proceedings of the 21st International Joint Conference on Artificial Intelligence, San Francisco, CA, USA, (pp. 2052V-2057).
[11] Bin Li, Qiang Yang, and Xiangyang Xue. (2009). Transfer learning for collaborative filtering via a rating-matrix generative model. ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning, New York, NY, USA, (pp. 617-624).
[12] J. Ben Schafer, Dan Frankowski, Jon Herlocker, and Shilad Sen. (2007). Collaborative filtering recommender systems. In The Adaptive Web, edited by Peter Brusilovsky, Alfred Kobsa, and Wolfgang Nejdl, Berlin, Heidelberg, Springer Berlin Heidelberg, (pp. 291-324).
[13] David W. Scott. (2009). Sturges' rule. WIREs Computational Statistics, (pp. 303-306).
[14] Meng-Lun Wu, Chia-Hui Chang, and Rui-Zhe Liu. (2011). Co-clustering with augmented data matrix. In DaWak'11: 13th International Conference on Data Warehousing and Knowledge Discovery, Toulouse, France.
[15] Jian-Hua Yeh, Meng-Lun Wu. (2010). Recommendation Based on Latent Topics and Social Network Analysis. In ICCEA'10: the Second International Conference on Computer Engineering and Applications, (pp. 209-213).

指導教授

張嘉惠(Chia-Hui Chang)

審核日期

2011-8-3

推文