基於貝氏網路之圖書推薦系統

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Title:	基於貝氏網路之圖書推薦系統
Other Titles:	A book recommender system based on the Bayesian network
Authors:	劉羣冠;Liu, Chun-Kuan
Contributors:	淡江大學資訊管理學系碩士班魏世杰;Wei, Shih-Chieh
Keywords:	貝氏網路;協同推薦;內容推薦;推薦系統;Bayesian Network;Collaborative Recommendation;content-based recommendation;Recommender System
Date:	2016
Issue Date:	2017-08-24 23:45:31 (UTC+8)
Abstract:	本文將實做一個推薦系統，透過貝氏網路結合協同與內容推薦方法，可以在沒有使用者背景的情況下，依據「給定的一本書」，進行相關圖書的推薦，希望能增進使用者查詢借閱書籍之效率與提高圖書館的書本借閱率。因為目前一般圖書館少有推薦系統，且有推薦系統的圖書館大多針對使用者進行推薦，這樣使用者必須登入系統後才可有推薦清單。但使用者通常習慣在查詢書本時不想登入，以省去麻煩及遭追蹤的可能，所以希望可以透過大眾化書推薦書的方式，讓系統在不取得使用者背景的情況下進行推薦。本文透過離線實驗結果得出在貝氏網路架構下加入關鍵字與分類號節點資訊皆可提高其推薦結果且優於傳統推薦方法。本文也讓使用者進行線上實驗佐證本推薦方法的實用性。 In this work, we will present a recommender system which combines the collaborative and content-based methods using the Bayesian network. Given a book, it can provide related book recommendation without knowing the user identity so that users can look up the desired books more easily and the library books can be utilized more efficiently. Currently, few libraries provide a book recommender system. For those libraries which provide the recommender system, the user must login to receive the recommended book list. But users are accustomed to looking up the books without login to save the trouble or the risk of being tracked. This work aims to provide an anonymous way of book recommendation about a book without knowledge of the user identity. In our offline experiments, the results show that under the framework of the Bayesian network, adding the keyword nodes and the subject code nodes can help promote the recommendation performance when compared with the traditional recommendation methods. This work also conducts an online user experiment to demonstrate the usefulness of our recommendation method.
Appears in Collections:	[Graduate Institute & Department of Information Management] Thesis

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