基于协同过滤的可信Web服务推荐方法

为了实现对可信Web服务的推荐,在分析了Web服务推荐技术与电子商务推荐技术的不同的基础上,提出了一种基于协同过滤的可信Web服务推荐方法。首先,根据已有成果对待组装应用的可信需求进行评估,根据此需求对相似用户进行首次筛选;然后在首次筛选的用户中,根据用户使用服务后的评分数据和用户信息来对相似用户进行二次筛选,经过两次筛选得到最终推荐用户。在根据用户对服务的评分数据计算用户之间的相似性时,考虑了不同服务对于用户间相似性的贡献值;在根据用户信息计算用户之间的相似性时,考虑到用户信息之间非线性的特点,引入了欧几里得距离公式来计算其相似值;在产生推荐的过程中还考虑了不诚实用户和用户数不足的问题。模拟实验结果表明该方法能够有效地对可信Web服务进行推荐。     

一种动态混合QoS语义的Web服务个性化推荐模型

服务推荐是服务计算中的主要问题之一,当前大多针对功能属性进行推荐,而在Web服务的QoS属性方面考虑较少,并且不支持动态变化的QoS属性。基于动态混合QoS的语义Web服务个性化推荐模型,把语义Web技术引入Web服务中,在QoS监控器下,有效监测Web服务的QoS属性变化并动态更新Web服务的QoS属性。根据建立的用户兴趣模型,向用户推荐具有个性化的Web服务。此外,在个性化推荐系统中使用最广泛的协同过滤推荐技术基础上,对数据进行了一系列的预处理填充,而且充分考虑了不同时间的项目评分对推荐的影响。结合用户兴趣度和用户评分的相似性计算方法,并通过不同的权值来表示它们的重要程度,综合计算目标用户的最近邻居集合,最终对用户u产生推荐。该系统在一定程度上提高了服务推荐的效率和准确度并满足用户查询需求。     

基于用户使用历史与信誉评价的Web API推荐

随着网络上发布的Web API服务越来越多,如何推荐给开发者用户感兴趣、信誉度高的Web API服务,以构建高质量高可信的软件服务系统,成为一个具有挑战性的研究问题。为此,提出一种基于用户使用历史与信誉评价的Web API服务推荐方法。计算用户使用历史记录与Web API之间的相似度,获得Web API的用户兴趣值。综合用户的Web API评分,调用Web API的Mashup服务的评价贡献和Alexa统计的Web API访问流量,获得Web API的信誉评价值。根据Web API的用户兴趣值以及信誉评价值,实现Web API的排名与推荐。实验结果表明,该方法推荐的Web API用户兴趣度DCG值高于SR-Based方法,服务信誉度DCG值高于UI-Based方法。     

面向用户群体的Web服务推荐

针对Web服务推荐中服务用户调用Web服务的服务质量数据稀疏性导致的低推荐质量问题,提出了一种面向用户群体并基于协同过滤的Web服务推荐算法(WRUG)。首先,为每个服务用户根据用户相似性矩阵构建其个性化的相似用户群体;其次,以相似用户群体中心点代替群体从而计算用户群体相似性矩阵;最后,构造面向群体的Web服务推荐公式并为目标用户预测缺失的Web服务质量。通过对197万条真实Web服务质量调用记录的数据集进行对比实验,与传统基于协同过滤的推荐算法(TCF)和基于用户群体影响的协同过滤推荐算法(CFBUGI)相比,WRUG的平均绝对误差下降幅度分别为28.9%和4.57%;并且WRUG的覆盖率上升幅度分别为110%和22.5%。实验结果表明,在相同实验条件下WRUG不仅能提高Web服务推荐系统的预测准确性,而且能显著地提高其有效预测服务质量的百分比。     

一种基于Bhattacharyya系数和项目相关性的协同过滤算法

在大数据时代,为了满足用户的信息需求,个性化推荐系统得到了广泛应用。协同过滤是一种简单有效的推荐算法。然而,许多传统的相似度计算方法仅仅基于用户的共同评分值,且不适用于稀疏数据环境,因此提出了一种新的基于Bhattacharyya系数的相似度方法。该方法使用了所有用户对项目的评分信息,不仅可以通过用户的评分行为获得用户的相似兴趣特征,而且可以获得用户已评分物品之间的相关性;同时由于不同的用户有不同的评分习惯,新方法也考虑了每个用户的评分偏好。通过考虑用户相似性的更多因素,可以为目标用户选择更恰当的邻域用户,以更有效地提升推荐性能。在两个真实数据集上进行的实验表明,所提方法优于其他当前最好的相似度方法。     

基于QoS感知的Web服务推荐算法

为了改进Web服务推荐的准确性,通过分析现有Web服务推荐算法的面临的主要问题,提出了一种基于地域划分的Web服务推荐方法。该算法对Web服务进行区域划分,并对区域内Web服务进行分类,根据用户区域位置,计算消费者之间及服务之间的相似程度,并以此相似度为基础对使用者并未调用过的服务的质量进行预测。基于大量真实数据的实验显示,该算法在推荐准确性方面优于现有的基于协同过滤的Web服务推荐算法。     

基于改进联合相似度计算的图书推荐算法

推荐系统可有效解决海量数据中的信息过载问题,为用户推荐感兴趣的信息。用户相似度计算是一种常用的推荐算法,但传统算法仅考虑用户-物品评分之间的相似性,忽略了用户固有特征对用户喜好的影响。考虑用户特征对推荐效果的影响,本文提出一种联合用户特征相似度及用户评分相似度计算的算法,采用F1衡量指标评价推荐效果的有效性。实验结果表明改进算法能有效提高推荐效果,取得更好的用户体验。     

面向不一致用户评价准则的在线服务推荐

客观上,用户的评价准则是由主观意识决定的,用户之间的评价准则不同导致多个用户对同一服务的评分不具备可比较性,不考虑不同用户评分的不可比较性所获得的服务推荐将难以满足用户个性偏好及其真实需求。为此,提出一种面向不一致用户评价准则的在线服务推荐方法,考虑用户偏好不一致时用户对在线服务的偏好关系,以偏好关系计算用户之间的相似度,并以此获得在线服务推荐结果。首先以用户-服务评分矩阵为基础建立用户对服务的偏好关系,其次根据偏好关系计算用户之间的相似度,然后以用户相似度为基础对用户未评分的服务进行评分预测,最后以预测评分的排序结果作为推荐结果。与经典的协同过滤推荐方法的比较实验,验证了本方法的有效性。实验表明,本方法获得的推荐结果能满足大多数用户的服务偏好,同时获得了比经典的协同过滤推荐方法更好的准确率。     

面向用户均衡需求的Web服务资源智能推荐方法

研究了满足用户在服务功能、服务属性和服务质量上的多层次均衡需求的Web服务资源智能推荐方法,解决了服务信息过载问题。现有服务推荐技术存在未考虑用户对服务多层次均衡需求的不足,结合Web服务资源的特点,综合考虑用户服务需求与候选服务群之间的多层次相似度,设计了综合Web服务资源的功能、属性和质量三个层次的适合度匹配算法,以及用户主观兴趣度匹配算法。通过这两个算法建立了面向用户均衡需求的个性化Web服务资源智能推荐方法,能将最大匹配度的Web服务资源推荐给目标用户。     

Method of intelligent recommendation of Web serviceresources towards users balanced requirements

研究了满足用户在服务功能、服务属性和服务质量上的多层次均衡需求的Web服务资源智能推荐方法, 解决了服务信息过载问题。现有服务推荐技术存在未考虑用户对服务多层次均衡需求的不足, 结合Web服务资源的特点, 综合考虑用户服务需求与候选服务群之间的多层次相似度, 设计了综合Web服务资源的功能、属性和质量三个层次的适合度匹配算法, 以及用户主观兴趣度匹配算法。通过这两个算法建立了面向用户均衡需求的个性化Web服务资源智能推荐方法, 能将最大匹配度的Web服务资源推荐给目标用户。     

Recommend trustworthy services using interval numbers of four parameters via cloud model for potential users

How to discover the trustworthy services is a challenge for potential users because of the deficiency of usage experiences and the information overload of QoE (quality of experience) evaluations from consumers. Aiming to the limitations of traditional interval numbers in measuring the trustworthiness of service, this paper proposed a novel service recommendation approach using the interval numbers of four parameters (INF) for potential users. In this approach, a trustworthiness cloud model was established to identify the eigenvalue of INF via backward cloud generator, and a new formula of INF possibility degree based on geometrical analysis was presented to ensure the high calculation precision. In order to select the highly valuable QoE evaluations, the similarity of client-side feature between potential user and consumers was calculated, and the multi-attributes trustworthiness values were aggregated into INF by the fuzzy analytic hierarchy process method. On the basis of ranking INF, the sort values of trustworthiness of candidate services were obtained, and the trustworthy services were chosen to recommend to potential user. The experiments based on a realworld dataset showed that it can improve the recommendation accuracy of trustworthy services compared to other approaches, which contributes to solving cold start and information overload problem in service recommendation.     

From similarity perspective: a robust collaborative filtering approach for service recommendations

Collaborative filtering (CF) is a technique commonly used for personalized recommendation and Web service quality-of-service (QoS) prediction. However, CF is vulnerable to shilling attackers who inject fake user profiles into the system. In this paper, we first present the shilling attack problem on CF-based QoS recommender systems for Web services. Then, a robust CF recommendation approach is proposed from a user similarity perspective to enhance the resistance of the recommender systems to the shilling attack. In the approach, the generally used similarity measures are analyzed, and the DegSim (the degree of similarities with top k neighbors) with those measures is selected for grouping and weighting the users. Then, the weights are used to calculate the service similarities/differences and predictions.We analyzed and evaluated our algorithms using WS-DREAM and Movielens datasets. The experimental results demonstrate that shilling attacks influence the prediction of QoS values, and our proposed features and algorithms achieve a higher degree of robustness against shilling attacks than the typical CF algorithms.     

一种基于情境的语义Web服务发现方法

在普适计算环境下,如何结合情境信息以发现满足用户需求的服务,是目前Web服务研究的一个关键问题.提出一种基于情境的Web服务发现框架,采用本体进行情境建模,利用用户反馈信息进行权重计算,并给出了一个基于相似度的语义Web服务的匹配算法.最后通过实验验证了该方法的有效性.     

A trustworthy QoS-based collaborative filtering approach for web service discovery

Many network services which process a large quantity of data and knowledge are available in the distributed network environment, and provide applications to users based on Service-Oriented Architecture (SOA) and Web services technology. Therefore, a useful web service discovery approach for data and knowledge discovery process in the complex network environment is a very significant issue. Using the traditional keyword-based search method, users find it difficult to choose the best web services from those with similar functionalities. In addition, in an untrustworthy real world environment, the QoS-based service discovery approach cannot verify the correctness of the web services’ Quality of Service (QoS) values, since such values guaranteed by a service provider are different from the real ones. This work proposes a trustworthy two-phase web service discovery mechanism based on QoS and collaborative filtering, which discovers and recommends the needed web services effectively for users in the distributed environment, and also solves the problem of services with incorrect QoS information. In the experiment, the theoretical analysis and simulation experiment results show that the proposed method can accurately recommend the needed services to users, and improve the recommendation quality.     

Integrating implicit feedbacks for time-aware web service recommendations

An increasing number of Web services have been published on the Internet over the past decade due to the rapid development and adoption of the SOA (Services Oriented Architecture) standard. However, in the current state of the Web, recommending suitable Web services to users becomes a challenge due to the huge divergence in published content. Existing Web services recommendation approaches based on collaborative filtering are mainly aiming to QoS (Quality of Service) prediction. Recommending services based on users’ ratings on services are seldomly reported due to the difficulty of collecting such explicit feedback. In this paper, we report a data set of implicit feedback on real-world Web services, which consist of more than 280,000 user-service interaction records, 65,000 service users and 15,000 Web services or mashups. Temporal information is becoming an increasingly important factor in service recommendation since time effects may influence users’ preferences on services to a large extent. Based on the collected data set, we propose a time-aware service recommendation approach. Temporal information is sufficiently considered in our approach, where three time effects are analyzed and modeled including user bias shifting, Web service bias shifting, and user preference shifting. Experimental results show that the proposed approach outperforms seven existing collaborative filtering approaches on the prediction accuracy.     

一种考虑QoS数据可信性的服务选择方法

随着Internet上功能相似的Web服务的逐渐增多,在运行时刻基于服务质量(QoS)对Web服务进行查找和选择已成为研究热点.现有的基于QoS的服务选择方法通常假定服务提供者和使用者给出的QoS数据都是真实可信的,然而这一假设在实际中往往很难保证.为此,提出了一种考虑QoS数据可信性的服务选择方法.方法从QoS数据来源的角度对质量属性进行分类和计算:对于数据来自服务提供者的质量属性,使用以往运行数据统计,对提供者的QoS数据进行修正;对于数据来自服务使用者的质量属性,通过计算用户间以往反馈的相似程度权衡不同QoS反馈数据的可信程度.对此给出了实现框架,并通过一组模拟实验说明该方法能够有效地削弱不可信的QoS数据对服务选择的影响,增强了Web服务选择结果的准确性.     

基于用户空间位置评分云模型的Web服务协同过滤推荐算法

Web服务作为无形的产品,不具备真实环境下的空间地理位置坐标,针对服务推荐中无法衡量用户群体与Web服务之间的距离位置关系,造成用户相似度计算失衡,导致推荐不准确等问题,提出了基于用户空间位置评分云模型的Web服务协同过滤推荐算法.首先基于用户群体的行为数据量化Web服务的热度区域,通过空间位置量化评分描述用户对于Web服务的兴趣偏好;其次利用云模型来描述每个用户空间行为评分的整体特征,设计了云模型间相似贴近度的计算方法,基于该方法提出了一种用户差异程度系数评估算法,并作为调控系数优化了皮尔森相似度量;最后通过协同过滤找出用户感兴趣的Web服务.实验结果表明该算法使得用户行为偏好的区域划分更加精确,在推荐准确率上明显提高,为基于位置的Web服务推荐提供新颖的方案.     

基于协同过滤的自适应Web服务QoS预测方法

随着Web服务越来越多,服务质量QoS作为描述Web服务的非功能性属性变得越来越重要。通常,一种服务的QoS对用户来说是未知的,因此对于基于Web服务的应用,精确预测其未知的QoS对于成功部署该服务具有重要的价值。基于协同过滤的WSRec算法是一种高精度的QoS预测方法,为进一步提升QoS的预测精度,提出了一种协同过滤的自适应Web服务QoS预测方法。该方法通过客户端首先发出QoS-Web服务请求;服务端接到请求后,根据已有数据,计算两两用户或服务间的相似度;并根据相似性找到对于目标用户的K个最接近用户或服务,生成该QoS值预测值A;同时在计算相似性时,采用改进皮尔逊相关系数得到预测值B;最后将预测值A和B以权值相结合得到目标用户或服务的QoS值。该算法改进了单一的协同过滤在数据稀疏的情况下,对相似性给予过高估计的不足,使得QoS预测值精度得以提高,取得了更好的实验结果。实验表明该方法预测精度优于WSRec算法。     

CluCF: a clustering CF algorithm to address data sparsity problem

In QoS-based Web service recommendation, predicting Quality of Service (QoS) for users will greatly aid service selection and discovery. Collaborative filtering (CF) is an effective method for Web service selection and recommendation. Data sparsity is an important challenges for CF algorithms. Although model-based algorithms can address the data sparsity problem, those models are often time-consuming to build and update. Thus, these CF algorithms aren’t fit for highly dynamic and large-scale environments, such as Web service recommendation systems. In order to overcome this drawback, this paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user (the new service) by location factor to lower the time complexity of updating clusters. Additionally, in order to improve the prediction accuracy, CluCF employs time factor. Time-aware user-service matrix Mu;s(tk, d) is introduced, and the time-aware similarity measurement and time-aware QoS prediction are employed in this paper. Since the QoS performance of Web services is highly related to invocation time due to some time-varying factors (e.g., service status, network condition), time-aware similarity measurement and time-aware QoS prediction are more trustworthy than traditional similarity measurement and QoS prediction, respectively. Since similarity measurement and QoS prediction are two key steps of neighborhood-based CF, time-aware CF will be more accurate than traditional CF. Moreover, our approach systematically combines user-based and item-based methods and employs influence weights to balance these two predicted values, automatically. To validate our algorithm, this paper conducts a series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that our approach is capable of alleviating the data sparsity problem.