Web 2.0概念的圖書館個人化推薦系統

A Web 2.0-based Personalized Recommendation System for Library

羅子文(Tzu-Wen Lo) ； 柯皓仁(Hao-Ren Ke)

《臺北市立圖書館館訊》 24卷4期 (2007/06) Pp. 1-30

摘要

Web 2.0的精神係透過社群的集體力量，創造、分享並評論屬於使用者自身或他人觀點的內容。從Wikipedia可以證明，此種以群眾意見為基礎的內容創造及評論模式，不論在客觀數據及社會觀感中均具有可信的份量。在Web 2.0之前，個別使用者針對網路商店提供的推薦清單，並無法回饋有關推薦精確度的訊息，而其他使用者也無法自他人的意見回饋中獲益。因此，本文將Web 2.0的精神與個人化推薦系統相結合，並應用在圖書館推薦系統中。本文使用關聯規則探勘（Association Rule Mining）得出個別讀者的推薦清單，再經由讀者們對書籍難易度的評價與個別讀者設定難易度的等級，過濾出難易適中的推薦書籍；同時經由讀者們對書籍加註標籤（Tagging）等Web 2.0的活動，重新對館藏進行分類，使得圖書館的藏書以一種更貼近當代讀者的面目呈現，以消除讀者對圖書館的隔閡。筆者希望透過Abu圖書館推薦系統，由量變產生質變，透過社群參與難易度的評價，讓讀者自行決定書籍的適當閱讀順序與分類。讀者將透過更親切、容易的方式找書，同時也讓前人的閱讀經驗得以留存，幫助後進者的求知之路。

關鍵字

社會性標記 ； 推薦系統 ； 關聯規則探勘 ； 圖書館

並列摘要

True to the Web 2.0 spirit of creating, sharing, and tagging by open communities, contents of websites are no longer provided by site owners but users. Wikipedia, as one of the paradigms of Web 2.0 websites, was proved that this kind of running model which is made of people, tagging, and review by people has earned trustworthy reputation in objective data and general impression.Before Web 2.0 era, users could not response their feedbacks to recommendation lists of online stores, with the result that stores could not improve the system by collecting feedbacks.In order to solve the problem and offer an adaptive recommendation system that automatically adjusts recommendation results to users' preference by collecting response of users, this article combines Web 2.0 features with personal recommendation system and puts in use in library. First of all, the system applies association rule mining to obtain individual recommendation list. Secondly, the system filters out unsuitable results dependent on personal rating records, and in proportion to overall rating by all of the users. Therefore, the final recommendation list should be more close to each user. Furthermore, users' tagging may also influence the classification of books in a library catalog, which could break down the barrier between library and readers.

並列關鍵字

Web 2.0 ； Social Tagging ； Recommendation System ； Association Rule Mining ； Libraries

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