There are mostly two approaches to tackle trust management worldwide Strong
and crisp and Soft and Social. We analyze the impact of integrated trust
mechanism in three different e-commerce services. The trust aspect is a dormant
element between potential users and being developed expert or internet systems.
We support our integration by preside over an experiment in controlled
laboratory environment. The model selected for the experiment is a composite of
policy and reputation based trust mechanisms and widely acknowledged in
e-commerce industry. The integration between policy and trust mechanism was
accomplished through mapping process, weakness of one brought to a close with
the strength of other. Furthermore, experiment has been supervised to validate
the effectiveness of implementation by segregating both integrated and
traditional trust mechanisms in learning system

在控制实验室环境中进行了一个试验来验证整合政策和信任机制在学习系统中的有效性，并对信任管理、电子商务服务、政策和声誉基于信任机制等关键词进行了分析。

提高电子商务服务信任的综合信任机制的实证评估

Empirical Evaluation of Integrated Trust Mechanism to Improve Trust in  E-commerce Services

Helpful reviews have been essential for the success of e-commerce services,
as they help customers make quick purchase decisions and benefit the merchants
in their sales. While many reviews are informative, others provide little value
and may contain spam, excessive appraisal, or unexpected biases. With the large
volume of reviews and their uneven quality, the problem of detecting helpful
reviews has drawn much attention lately. Existing methods for identifying
helpful reviews primarily focus on review text and ignore the two key factors
of (1) who post the reviews and (2) when the reviews are posted. Moreover, the
helpfulness votes suffer from scarcity for less popular products and recently
submitted (a.k.a., cold-start) reviews. To address these challenges, we
introduce a dataset and develop a model that integrates the reviewer's
expertise, derived from the past review history of the reviewers, and the
temporal dynamics of the reviews to automatically assess review helpfulness. We
conduct experiments on our dataset to demonstrate the effectiveness of
incorporating these factors and report improved results compared to several
well-established baselines.

针对检测有用评价的问题，本研究提出了一个新的数据集和方法，该方法利用评论者的历史评价记录和评论的时间动态来自动评估评论的有用性，并展示了比多个基准方法更好的效果。