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Novel personal and group-based trust models in collaborative filtering for document recommendation

机译：协作过滤中用于文档推荐的新型个人和基于组的信任模型

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摘要

Collaborative filtering (CF) recommender systems have been used in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques in order to improve recommendation quality. Some researchers have proposed rating-based trust models to derive trust values based on users' past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships; however, 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 personal trust model which adaptively 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; thus, users' preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendations from the group perspective. We then propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experimental results show that the proposed models can improve the prediction accuracy of other trust-based recommender systems.
机译：协作过滤（CF）推荐器系统已在各种应用程序领域中使用，以解决信息过载问题。最近，基于信任的推荐器系统已将用户的可信度纳入CF技术中，以提高推荐质量。一些研究人员提出了基于等级的信任模型，以基于用户过去对商品的等级或基于明确指定的关系（例如朋友）或信任关系来得出信任值;但是，基于评级的信任模型可能无法在CF推荐中有效，因为过去很少的评级记录产生了不可靠的信任值。在这项工作中，我们提出了一种混合型个人信任模型，该模型将基于评级的信任模型和显式信任度量自适应地结合起来，以解决由于过去的评级记录不足而导致的弊端。此外，具有相似偏好的用户通常会组成一个小组来彼此共享项目（知识）。因此，用户的偏好可能会受到组成员的影响。因此，团体信任可以增强个人信任，以从团体角度支持建议。然后，我们提出了一种基于个人和团体信任的混合模型的推荐方法，以提高推荐性能。实验结果表明，所提出的模型可以提高其他基于信任的推荐系统的预测精度。

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