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A trust-based architectural framework for collaborative filtering recommender system

机译：基于信任的协作过滤推荐系统的体系结构框架

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摘要

Recommender systems have been used to suggest the interesting items such as movies, books and songs according to the choice of users. These systems compute a user similarity among users and use it as a weight for the users' ratings. However, they have many weaknesses, such as sparseness, cold start and vulnerability to attacks. The traditional recommender system techniques are often ineffective and are not able to compute a user similarity weight for many of the users. The trust among two or more users in the web of trust increases the quality of recommendation in two ways. Firstly, the trust metrics reduce the computability of similarity assessment of users or items. Secondly, the reputation of users may be computed using trust propagation. In this paper, architecture of trust-based recommender systems is proposed. In which trust metrics and rating matrix are taken as input and neighbours are generated using trust metrics and user similarity respectively and importance of trust over collaborative filtering is described. In the proposed approach, trust-based issues are discussed to solve the problem of traditional recommender system such as, data sparsity, cold-start users, malicious attacks on recommender systems and centralised architectures.
机译：推荐系统已经根据用户的选择来建议有趣的项目，例如电影，书籍和歌曲。这些系统计算用户之间的用户相似度，并将其用作用户评级的权重。但是，它们具有许多缺点，例如稀疏，冷启动和容易受到攻击。传统推荐系统技术通常无效，并且无法为许多用户计算用户相似权重。信任网络中两个或多个用户之间的信任以两种方式提高了推荐的质量。首先，信任度量降低了用户或项目的相似性评估的可计算性。其次，可以使用信任传播来计算用户的信誉。本文提出了一种基于信任的推荐系统体系结构。其中以信任度和评分矩阵为输入，并分别使用信任度和用户相似度生成邻居，并描述了信任对协同过滤的重要性。在提出的方法中，讨论了基于信任的问题，以解决传统推荐系统的问题，例如数据稀疏性，冷启动用户，对推荐系统和集中式体系结构的恶意攻击。

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