标题: | 整合个人与群体信任模式之文件推荐 Document Recommendations based on Hybrid Personal and Group Trust Models |
作者: | 林采馨 Lin,Cai Sin 刘敦仁 Liu,Duen Ren 资讯管理研究所 |
关键字: | 协同过滤;信任推荐方法;文件推荐;群体推荐方法;Collaborative Filtering;Trust-based Recommender System;Document recommendation;Group Recommendation, Role Relationship |
公开日期: | 2010 |
摘要: | 协同过滤推荐系统已被广泛用来改善资讯过载的问题,而其主要的概念为依据相似兴趣使用者的喜好来预测目标使用者的喜好以进行推荐。近来已有学者提出以信任机制导入协同式过滤推荐来提升推荐品质,有些研究主要是提出以历史评分记录来预测评分的准确度做为衡量使用者之间的信任值,有些研究则是提出以使用者关系如朋友关系或是由使用者描述之信任值,结合使用者喜好相似度来进行推荐,然而鲜少有相关研究结合由历史评分纪录所推导之信任值及使用者关系信任值来进行推荐。当使用者历史评分记录非常少时,由历史评分纪录所推导之信任值将不可靠而无法据以进行推荐。本研究提出结合两项信任值以形成个人角度的混合信任值,藉由使用者关系所定义之信任值,来改善由历史评分纪录推导信任值之缺失。除此之外,喜好相似之使用者通常以群体来进行知识文件之分享,因此,以群体角度分析推荐者的信任值,将可补强个人信任值之不足,然而鲜少有相关研究整合个人和群体信任值来设计推荐机制。本研究进一步提出整合群体和个人信任值之协同过滤推荐方法,以提升推荐之品质。实验评估结果显示所提的方法比传统以信任机制为基础的协同过滤推荐方法能更有效提升推荐的品质。 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. |
URI: | http://140.113.39.130/cdrfb3/record/nctu/#GT079834514 http://hdl.handle.net/11536/47920 |
显示于类别: | Thesis |