基于社会网络的WebService推荐模型研究

发布时间：2018-06-19 21:54

本文选题：WebService推荐模型 + 信任计算　；参考：《哈尔滨工程大学》2016年硕士论文

【摘要】：最近几年,分布式技术快速发展,WebService技术作为一种新型的分布式系统计算模型,其使用规模也在企业管理以及电子商务领域快速扩大,WebService推荐模型也应时而生,在WebService的推广方面起到了巨大的作用。传统WebService推荐模型分为两种,一种是基于WebService功能的推荐,另一种则是基于协同过滤算法的推荐。但是这两种推荐模型也存在各自的问题,基于WebService功能的推荐模型受限于完备的功能描述本体库以及准确的功能分析匹配算法,基于协同过滤的推荐模型则会受到数据密度以及系统冷启动问题的影响。为了避免这些问题对推荐系统的巨大影响,本文另辟蹊径,选择时下热门的社会网络技术作为推荐参考,根据社会网络中相似关系和信任关系对推荐参数的影响设计算法,提出基于社会网络的WebService推荐模型。首先研究了社会网络中信任关系的度量方式和信任在社会网络中的传播机制。在直接信任关系度量方面,本文根据用户公共好友列表规模来进行计算。而信任传播机制研究主要分为串行信任计算和并行信任计算两个方面,分别选取距离比例法和取最大值法进行计算。其次研究了社会网络中相似关系的度量方式,分析了传统相似性算法例如皮尔逊相关系数和余弦夹角相似性的不足之处,然后结合WebService的特点以及传统相似性算法设计了基于Qos属性参数的相似度计算公式,用来度量用户之间相似度以及服务之间相似度。然后针对算法在数据量增大的情况下出现的性能方面下降的问题,提出了基于分团的相似度网络过滤方式,将具有相同兴趣的用户放在相同分团之中,同时根据设置阈值因子对分团中的相似度进行限制,最终过滤掉相似度较低节点之间的相似关系,提高相似度算法的准确程度。结合两个用户节点之间相似推荐度和信任推荐度计算出综合推荐度,然后联合综合推荐度与WebService质量评估模型的属性参数计算WebService的推荐度,最终根据推荐度高低进行WebService推荐。最后本文针对三个模型设计相关实验,分别对推荐结果的精确程度进行对比与分析,验证了本文设计WebService推荐模型在推荐精度方面的性能优化。
[Abstract]:In recent years, distributed technology has developed rapidly as a new type of distributed system computing model, and its scale of use has also expanded rapidly in the field of enterprise management and electronic commerce. In the promotion of Web Service has played a huge role. Traditional Web Service recommendation models can be divided into two types, one is recommendation based on Web Service function, the other is recommendation based on collaborative filtering algorithm. However, these two recommendation models also have their own problems. The recommendation model based on Web Service function is limited by the complete function description ontology library and the accurate function analysis and matching algorithm. The recommendation model based on collaborative filtering is affected by the data density and the cold start problem of the system. In order to avoid the huge influence of these problems on the recommendation system, this paper chooses the popular social network technology as the recommendation reference, and designs the algorithm according to the influence of the similarity relation and the trust relation on the recommendation parameter in the social network. A web service recommendation model based on social network is proposed. Firstly, the measurement of trust relationship in social network and the communication mechanism of trust in social network are studied. In terms of direct trust measurement, this paper calculates the size of the user's list of public friends. The research of trust propagation mechanism is mainly divided into serial trust computation and parallel trust computing. Distance ratio method and maximum value method are selected to calculate the trust propagation mechanism. Secondly, the measurement of similarity in social networks is studied, and the shortcomings of traditional similarity algorithms such as Pearson correlation coefficient and cosine angle similarity are analyzed. Then, combining the characteristics of Web Service and the traditional similarity algorithm, the similarity calculation formula based on QoS attribute parameters is designed to measure the similarity between users and services. Then aiming at the problem that the performance of the algorithm decreases when the amount of data increases, a similarity network filtering method based on clustering is proposed, in which users with the same interest are placed in the same group. At the same time, according to setting threshold factor to limit the similarity in the cluster, filter out the similarity relationship between the lower nodes, and improve the accuracy of the similarity algorithm. Combining the similarity recommendation degree and trust recommendation degree between two user nodes, the comprehensive recommendation degree is calculated, then the recommendation degree of Web Service is calculated by combining the attribute parameter of the comprehensive recommendation degree with the attribute parameter of the Web Service quality evaluation model, and finally the Web Service recommendation is carried out according to the recommendation degree. Finally, this paper designs related experiments for the three models, compares and analyzes the accuracy of the recommended results, and verifies the performance optimization of the Web Service recommendation model designed in this paper.
【学位授予单位】：哈尔滨工程大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.3;TP393.09

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