(第 25 期)   第十三卷第一期   2018 年 12 月 31 日出刊

基於多層關聯探勘之新穎圖書推薦系統

A Novel Book Recommendation System Based on Multi-level Association Mining

本文關鍵字:協同過濾資料稀疏性多層次關聯規則圖書推薦學術圖書館Collaborative FilteringSparsityMultiple-Level Association Rule MiningBook RecommendationAcademic Library

本文摘要

在當今學術圖書館圖書借閱率逐年下降,圖書推薦系統顯的更加重要—它能有效協助機構促進圖書利用與決策支援。協同過濾為最成功與廣泛應用之推薦系統架構,協同過濾以相同閱讀興趣讀者對圖書的評價資料做為圖書推薦之計算基礎,但學術圖書館在典藏政策、讀者借閱行為、與營運模式均有別於一般商業書店,最為特殊之處在於學術圖書借閱交易的資料稀疏性與分佈不平衡,這兩個現象嚴重影響圖書推薦預測之準確度與品質。本研究提出以學術圖書館標準分類系統作為基礎,並結合多層次關聯規則探勘演算法,以解決資料稀疏性及分佈不均的問題。此外,本研究設計最佳化折衷方案之圖書推薦策略,作為推薦圖書之挑選依據,再利用隱含性指標資訊之回饋機制調整計算權重,藉以提高圖書推薦之準確度及有效性。實驗證明所提出的方法能有效的降低資料稀疏程度,並且能更準確地發掘讀者感興趣之潛在圖書清單,進而達成有效的圖書推薦。

As the borrowing rate at libraries declines significantly year by year, the book recommendation system becomes increasingly important in that it can help libraries to promote the borrowing and utilization of books. Collaborative filtering is one of the most successful and widely used technologies for recommendation systems. It leverages the similarity in reading taste between readers as the basis of recommendation. However, this method can’t directly apply to book recommendation in academic libraries because their collection policy, reader’s behavior, and business model are different from business bookstores. In particular, the distributions of borrowed books at academic libraries are sparse and imbalanced, which seriously affect the accuracy and quality of book recommendation. This paper proposes a new method for book recommendation at academic libraries. The proposed method incorporates multi-level association rule exploration algorithm and taxonomy tree to address the issues of sparsity and imbalance. In addition, we present a best compromise solution for selection of recommended books. The feedback from implicit indicator information is further used to improve the accuracy and effectiveness of recommendation. Experimental results show that the proposed method can handle the sparsity and imbalance in transactions and discover the books of the reader’s potential interest, which validates the utility and effectiveness of recommendation.
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