整合個人與群體信任模式之文件推薦

Abstract

協同過濾推薦系統已被廣泛用來改善資訊過載的問題，而其主要的概念為依據相似興趣使用者的喜好來預測目標使用者的喜好以進行推薦。近來已有學者提出以信任機制導入協同式過濾推薦來提升推薦品質，有些研究主要是提出以歷史評分記錄來預測評分的準確度做為衡量使用者之間的信任值，有些研究則是提出以使用者關係如朋友關係或是由使用者描述之信任值，結合使用者喜好相似度來進行推薦，然而鮮少有相關研究結合由歷史評分紀錄所推導之信任值及使用者關係信任值來進行推薦。當使用者歷史評分記錄非常少時，由歷史評分紀錄所推導之信任值將不可靠而無法據以進行推薦。本研究提出結合兩項信任值以形成個人角度的混合信任值，藉由使用者關係所定義之信任值，來改善由歷史評分紀錄推導信任值之缺失。除此之外，喜好相似之使用者通常以群體來進行知識文件之分享，因此，以群體角度分析推薦者的信任值，將可補強個人信任值之不足，然而鮮少有相關研究整合個人和群體信任值來設計推薦機制。本研究進一步提出整合群體和個人信任值之協同過濾推薦方法，以提升推薦之品質。實驗評估結果顯示所提的方法比傳統以信任機制為基礎的協同過濾推薦方法能更有效提升推薦的品質。

Collaborative filtering (CF) recommender systems have been applied in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques to improve the quality of recommendation. Some researchers have proposed rating-based trust models to derive the trust values based on users’ past ratings of items, while some researchers use explicit trust metric to derive the trust values based on explicitly specified relations (e.g. friends) or trust relationships. However, conventional trust-based CF did not investigate how to combine the rating-based trust model with explicit trust metric to derive the trustworthiness of users. The rating-based trust model may not be effective in CF recommendations, due to unreliable trust values derived from very few past rating records. In this work, we propose a hybrid trust model which combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. Moreover, users with similar preferences usually form a group to share items (knowledge) with each other, and thus users’ preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendation from group perspective. Nevertheless, conventional trust-based CF did not address trust computation by considering both personal and group trust. Therefore, we propose a recommendation method based on a hybrid model of personal and group trust to improve the recommendation performance. The experiment result shows that the proposed model can improve the prediction accuracy of CF method compared with other trust-based recommender systems.