中文摘要：

社会网络环境中,由于缺乏有效的激励策略,导致节点缺乏服务交易的主动性和积极性;由于存在利益相关,一些节点会相互联合去共同推荐某个节点,形成协同作弊和信任推荐不可信的问题.为此,提出了社会网络中交易节点的选取及其信任关系计算方法.通过设计竞标服务策略来调动节点提供资源服务的积极性.针对不同服务中评价指标权重难以客观确定的问题,提出基于熵权的指标权重确定方法,并利用TOPSIS方法来选择合适的网络交易节点以避免交易节点选取的随意性.由于信任与风险并存,因此引入交易影响力函数来融合节点的直接信任和推荐信任以确定所选交易节点的可信性,并通过考虑推荐时间影响函数、交易内容相似度和推荐熟悉度等多维影响因子来保证推荐可信性.最后提出了基于多属性的节点推荐信任度更新方法.仿真结果表明文中方法对提高节点服务积极性、抑制节点协同作弊和恶意欺诈都有较好的效果.

英文摘要：

Abstract In current social network, the mutual trust relationship is the basis of service transac- tion. The lack of effective incentive strategy can lead to little incentive and enthusiasm of nodes. Meanwhile, some nodes probably recommend the certain node in evaluating trustworthiness of others because of the interrelated interests, also referred as collaborative cheating, which is likely to cause untrustworthy issues of recommendation. In order to solve this problem, we propose se- lecting transaction nodes and trust computing between them in social network. First of all, serv ice transaction nodes acquire service opportunity and gain payment through bidding method, which can incentivize enthusiasm of service transaction nodes. Secondly, a novel transaction node selecting method based on entropy weight and TOPSIS to avoid random selecting and select the appropriate node. Then, the transaction influential functions are brought in to fuse direct trust and recommendation trust of nodes. In which, we can avoid malicious recommendation and col- laborate cheating by comprehensive consideration of multi-dimensions factors, including recom- mendation time influential function, transaction content similarity and recommendation acquaint-ance. Furthermore, we propose a recommendation trustwo multi-attributions. The experiment results demonstrate good in restraining malicious cheating, malicious recommendation recommendation trust updating issue can be ensured. rthiness updating method effect of trust computing and collaborative cheating ased on gorithm and the recommendation trust updating issue can be ensured.