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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 鄭卜壬 | |
dc.contributor.author | Keng-Wei Cheng | en |
dc.contributor.author | 鄭耿維 | zh_TW |
dc.date.accessioned | 2021-06-16T17:58:03Z | - |
dc.date.available | 2014-08-19 | |
dc.date.copyright | 2012-08-19 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-10 | |
dc.identifier.citation | [1] P. J. Michael and B. Daniel, Content-based recommendation systems. In The Adaptive Web:Methods and strategies of web personalization. Volume 4321 oF Lecture Notes in Computer Science, pages 325-341., Springer-Verlag, 2007.
[2] M. J. Raymond and R. Loriene, 'Content-Based Book Recommending Using Learning for Text Categorization,' in Proceedings of the 8th int. conf. on Intelligent user interfaces, p. 244, ACM, 2003. [3] F. Michael and H. Eduard, 'Recommendations Without User Preferences:A Natural Language Processing Approach,' in Proceedings of the 8th int. conf. on Intelligent user interfaces, p. 244., ACM, 2003. [4] L. Chin-Yew and H. Eduard, The Automated Acquisition of Topic Signatures for Text Summarization., COLING, 2000. [5] W. Christian, S. Wout and W. Martin, 'Selecting keywords for content based recommendation. In: CIKM (2010), p. 1533-1536'. [6] D. Souvik, G. Niloy and M. Pabitra, 'Feature Weighting in Content Based Recommendation System Using Social Network Analysis,' in World Wide Web, ACM, 2008. [7] L. Beibei, G. Anindya and I. G. Panagiotis, 'Towards a Theory Model for Product Search,' in World Wide Web, ACM, 2011. [8] H. Thomas, 'Probabilistic Latent Semantic Analysis. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 289-296, 1999.'. [9] B. M. David, N. Y. Andrew and J. I. Michael, 'Latent Dirichlet Allocation,' Journal of Machine Learning Research, vol. 3, p. 993–1022, 2003. [10] C. Chih-Chung and L. Chih-Jen, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. [11] H. Mark, F. Eibe, H. Geoffrey, P. Bernhard, R. Peter and H. Ian, The WEKA Data Mining Software: An Update; SIGKDD Explorations, Volume 11, Issue 1.. [12] C. E. Shannon and W. Warren, 'The Mathematical Theory of Communication.,' in Urbana, Illinois: University of Illinois Press, 1949. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64612 | - |
dc.description.abstract | 在資訊爆炸的時代,一個商業平台若想要能夠在眾多的競爭中異軍突起,就必須要能夠更快速的捕捉到使用者的偏好,雖然說在使用者的基本資料並不會訴說他們本身具有甚麼樣的偏好,不過在很多商業平台上其實都有紀錄著使用者的交易紀錄,藉由商品特徵中所隱含的概念我們便可間接的探知使用者的偏好,但是在目前的一些平台上,通常都沒有特意藉由交易記錄去了解使用者的需求,在產品搜尋以及產品推薦方面通常都只藉由單一的準則(價格或時間)。雖然過往有些研究也試著想去捕捉使用者的喜好,但是它們都存在著一些問題,有些是藉由關鍵字來表示而沒有去捕捉其中的語意,有些雖然有考量語意但是卻需要花費大量的人工。因此在我們的研究中,我們提出了一個方法,藉由這個方法我們可以自動化地從交易紀錄去挖掘出商品特徵所隱含的概念,而這就間接代表了使用者的偏好,最後我們便可以藉由這些知識為使用者改善產品搜尋的結果,進而提升商業平台的競爭能力。 | zh_TW |
dc.description.abstract | Information nowadays increase rapidly, a commerce site should capture the
preference of user as soon as possible if he want to be a good one in the world. Although user rarely describe his preference in his personal information, but most commerce site has stored the transaction log of user, so we can mine the preference of user from the transaction logs. But most of them do not utilize the rich resource, their recommendation or product search are only based on single criteria , like popularity, price, or time…etc. Some previous research also try to mine the preference from the transaction logs, but they all exist some problems, some of them are keyword-based representation, this method do not consider the semantic meaning of word. Some are type-based representation, although this method has considered the semantic meaning of word, but they need to spend a lot of time to analysis the description of product. We propose a method which can automatically mine the concept of product feature, and use it to represent user preference. We can finally use the knowledge we have mined to improve the performance of product search. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:58:03Z (GMT). No. of bitstreams: 1 ntu-101-R99922109-1.pdf: 847284 bytes, checksum: f166ccea20e965ef423314f19bf3d6d0 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii 目錄 iv 圖目錄 vi 表目錄 vii 第一章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 3 1.3 論文架構 3 第二章 文獻探討 5 2.1 以關鍵字為基礎的表示方法(Keyword-based) 5 2.2 以類型為基礎的表示方法(Type-based) 7 第三章 問題 9 3.1 實驗語料 9 3.1.1 資料爬取 9 3.1.2 實驗語料形式 9 3.2 問題描述 10 第四章 研究方法 12 4.1 資料集的預處理 12 4.2 擷取產品類別特徵 13 4.2.1 賣家名稱 13 4.2.2 商品品牌名稱 15 4.2.3 地區類別特徵以及交貨方式特徵 17 4.2.4 商品觀點特徵(Aspects) 22 4.2.5 商品性別傾向特徵 27 4.3 偏好模型(Preference Model) 28 4.3.1 向量空間模型 29 4.3.1.1 關鍵字擷取 29 4.3.1.2 建立向量空間模型 29 4.3.2 負向資料擷取 30 第五章 實驗 31 5.1 訓練資料集與(Training data)測試資料集(Testing data) 32 5.2 實驗效能 33 5.2.1 分類準確性實驗 33 5.2.2 產品搜尋效能實驗以及商品推薦效能實驗 34 5.2.3 類別特徵重要性實驗 39 5.2.4 推薦效能與交易紀錄數量對比實驗 41 第六章 結論與未來研究方向 42 6.1 研究結論 42 6.2 未來研究方向 42 參考文獻 44 | |
dc.language.iso | zh-TW | |
dc.title | 基於交易紀錄自動發掘使用者購買喜好
以增進產品搜尋的效能 | zh_TW |
dc.title | Automatically Mining User Preference from Transaction
Log for Improving Performance of Product Search | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 魏志達,邱志義 | |
dc.subject.keyword | 使用者偏好,產品搜尋,以內容為基礎,推薦系統,未標示的資料, | zh_TW |
dc.subject.keyword | User preference,Product search,Content-based,Recommendation,Unlabeled data, | en |
dc.relation.page | 45 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-10 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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