基于消费者社区的云服务选择模型CC-PSM
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CC-PSM：云服务选择模型基于消费者社区的偏好感知
CC-PSM（Consumer Community-based Preference-aware Selection Model）是一种基于消费者社区的云服务选择模型，旨在选择满足目标消费者需求和偏好的云服务。该模型考虑了消费者个人的偏好，并提高了服务预测的可靠性。
在CC-PSM模型中，首先从双分网络中挖掘出云消费者之间的关联，以计算出每个消费者的偏好权重。然后，根据消费者的偏好权重和云服务的特征，计算出每个云服务的匹配度。最后，选择匹配度最高的云服务，以满足目标消费者的需求和偏好。
CC-PSM模型的优点在于，它能够充分考虑消费者的个体偏好，提高了服务预测的可靠性。同时，CC-PSM模型也能够处理大规模的云服务选择问题，提高了云服务选择的效率。
CC-PSM模型的应用场景包括云计算、云存储、云数据库等云服务选择领域。该模型能够帮助云服务提供商更好地了解消费者的需求和偏好，提高云服务的质量和可靠性。
在CC-PSM模型的实现中，需要考虑到以下几个关键技术点：
1. 云消费者之间的关联挖掘：该步骤需要使用数据挖掘算法来挖掘出云消费者之间的关联，并计算出每个消费者的偏好权重。
2. 云服务特征提取：该步骤需要使用机器学习算法来提取云服务的特征，以便计算出云服务的匹配度。
3. 匹配度计算：该步骤需要使用算法来计算出每个云服务的匹配度，以便选择匹配度最高的云服务。
CC-PSM模型是一种基于消费者社区的云服务选择模型，能够充分考虑消费者的个体偏好，提高了服务预测的可靠性。该模型具有广泛的应用前景，能够提高云服务的质量和可靠性。
此外，CC-PSM模型还可以与其他技术集成，例如机器学习、自然语言处理等，以提高云服务选择的智能化和自动化程度。例如，可以使用机器学习算法来预测消费者的偏好，或者使用自然语言处理技术来分析消费者的反馈信息，以提高云服务选择的准确性。
CC-PSM模型是一种基于消费者社区的云服务选择模型，能够提高云服务选择的智能化和自动化程度，提高云服务的质量和可靠性。
Mathematical Problems in Engineering
preference through comparing the users’ ranking similarity of
thecommonlyinvokedservicesandthenmakesQoSranking
prediction of a set of cloud services for the target user based
on the similar users’ ranking with him.
To eliminate a sharp trust crisis between cloud consumers
and cloud service providers, trust is introduced in the process
of service selection. In [], a novel cloud service-compo-
sition method based on the trust span tree was proposed.
roughthecrediblerelationshipevolution,thetrustunion
of service providers with same or similar function is formed,
which helps to exclude uncertain or malicious service from
the trust span tree. But trust is a subjective cognition judg-
ment. erefore, the trust evaluation needs not only objective
measurement but also subjective perception. Ding et al. []
designed a novel framework named CSTrust for conducting
cloud service trustworthiness evaluation by combining QoS
prediction and customer satisfaction estimation. To reduce
thebiascausedbyunreasonablefeedbackfromunprofes-
sional or malicious cloud users, a method was proposed for
ltering the feedback from such users in []. Aer process-
ing, the aggregated result can quantitatively reect the overall
quality of a cloud service.
Whether a service is credible to satisfy the consumer’s
personalitypreferenceornotisthemainconsideration
in []. is paper utilized fuzzy clustering technology to
classify services by the requesters’ preferences and gave a
service selection algorithm to select a closest classication
with the requester’s preference. With the integration of trust
evaluation method and CF technique, Wang and Zhang
[] presented a trustworthy service selection model based
on collaborative ltering. is model introduces consumer
correlation to embody the impact of the requester’s personal
characteristics on selection process, computes creditability
of recommendation, and employs analytic hierarchy process
to decide the weight of each factor in service reputation.
Abedinzadeh and Sadaoui [] presented ScubAA, a novel
generic agent trust management framework based on the the-
ory of human plausible reasoning. ScubAA recommends to
the user a list of the most trusted services in terms of a single
personalized value derived from several types of evidences
such as user’s feedback, history of user’s interactions, context
of the submitted request, references from third party users as
well as from third party service agents, and structure of the
society of agents.
As seen from the above literatures, selecting the appropri-
ate recommendation users is the key to the service selection
basedontrust.In[], an innovative idea for selecting the
reliable recommendation users was explored. e authors
thought that sparsity, cold-start and trustworthiness were
major issues challenging service recommendation in adopt-
ing similarity-based approaches. With the prevalence of
social networks, to a certain extent, the user’s characteristics
andpreferencewereexposedfromthedatainblogsand
social-networking sites. So a social network-based service
recommendation method with trust enhancement was pro-
posed in the paper. e method assesses the degree of trust
between users in social network by a matrix factorization, and
then recommendation results are obtained by an extended
random walk algorithm.
e above literatures used dierent technologies to realize
an intelligent service selection for consumers from dierent
angles, including quantifying the consumers’ demands or
preferences, predicting the quality of service, matching the
consumer’s demands and the service’s performance, and
recommending the services based on trustworthy consumers.
Dierent methods have dierent merits and limitations.
Firstly, the method based on the parameters matching
between the consumer’s demands and the service’s perfor-
mance is relatively simple and intuitive. But this type of
the method mostly requires the consumers to explicitly give
his/her QoS demands, which is too dicult for consumers to
provide these parameters. So its feasibility is poor in practice.
Secondly, the method by predicting the quality of service only
considers the general performance of the service but ignores
the dierences of QoS demands. irdly, the fuzziness and
uncertaintyofuserpreferenceimplythatitisadicultthing
tominetheusers’preferences.emethodsofquantifying
the consumers’ preferences are still under study. In addition,
there are still no ecient ways to validate the quantitative
results. Finally, the method based on trust recommendation
is now widely accepted in the eld of electronic commerce
and social networking service. us the method has good
application prospects in terms of cloud services selection.
e key problem of the method is the choice of trustworthy
customers for service recommendation. But the existing
methods for selecting the trustworthy customers are mostly
unstableandinecient,whichaecttheaccuracyofthe
selection results and the feasibility of the selection process.
From what has been discussed above, we can see clearly
that the core problem of service selection is to nd a
feasible method to identify dierent consumers’ preferences.
To resolve the problem, a cloud service selection model
basedonsimpleevaluationinformationandtheideaof
the trust in community network will be built in the paper.
At rst, we need to consider how to mine the consumers’
preferences based on available information in the cloud so
as to improve the feasibility of the method in practical
application. Inspired by the literature [], a consumer is a
mappingforarealpersoninrealsociety,whosebehaviorand
preference are relatively stable. Due to dierent profession
and background, the preferences of the consumers would
show some group features. So we can cluster the consumers
with similar preference to form a stable community, which
is the basis of service selection. According to the theory of
human plausible reasoning [], the consumer would trust
the consumers who are similar with him/her more than the
others. en we design a service selection model based on
community trust.
3. Community Discovery Model Based on
Consumer Preference
Predicting a consumer’s evaluation on an unknown service
mainly relies on the recommendations from other con-
sumers. So whether other consumers’ evaluations on a target
serviceareworthytotrustornotwillhavealargeimpacton
the accuracy of prediction. erefore it is a key step to nd
the trusted recommendation users in the process of service
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