Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

Cao, Jie; Wu, Zhang; Wang, Youquan; Zhuang, Yi

doi:10.1007/s10115-012-0562-1

Cited by 87 publications

(59 citation statements)

References 32 publications

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“…In this subsection, we compare our evaluation approach Vio_degree_eva with another two evaluation approaches, i.e., SD-HCF [8] (only quality point is considered) and FL-FL [9] (only quality curve is considered). Concretely, five evaluation profiles were tested.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Concretely, we consider a quality curve HR(q) corresponding to quality dimension q and historical record HR of cloud service CS. Then HR(q) is divided into K (K=10, 20, 30, 40, 50) discrete quality points, through which the user satisfaction degree with HR(q) could be calculated by (8). We compare the user satisfaction degree and the comparison results are shown in Figure 12.…”

Section: Profile5: User Satisfaction Degree With Respect To Kmentioning

confidence: 99%

“…Besides, we assume weight w i =1/L (1≤ i ≤ L) and ω j = 1/m (1≤ j ≤ m). For a cloud user, it is difficult to accurately evaluate his/her satisfaction degree with a quality curve by (8), as the service quality fluctuates continuously; therefore, sampling technique is recruited here for quality curve, so as to achieve approximate evaluation results. Some symbols recruited in the experiments as well as their specifications could be found in Table1.…”

Section: Cs) = Quality(cs) / (L*m) 14: Return Quality(cs)mentioning

confidence: 99%

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A Sla Violation Degree-Aware Cloud Service Evaluation Approach Based on Historical Records

Qi¹,

Dou²,

Zhang³

et al. 2014

STCC

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Cloud computing has provided a flexible and cost-effective resource provision manner to the public. From the perspective of cloud users, it is necessary to evaluate the cloud service's quality based on the service's historical execution records. However, the traditional evaluation approaches often assume that cloud service's historical quality is a fixed quality value (i.e., quality point), while neglects the long-term running characteristic of cloud service and the unpredictability of network environment, as well as the resulted fluctuant service quality (i.e., quality curve); this may decrease the completeness of cloud service evaluation. Besides, the traditional SLA (Service Level Agreement) contract manner only considers whether the agreed SLA is violated, while neglects the SLA violation degree, which may be unfair to cloud service providers. In views of the above two challenges, a SLA violation degree-aware cloud service evaluation approach Vio_degree_eva is put forward in this paper. Vio_degree_eva not only considers two kinds of historical records (i.e., quality point and quality curve), but also includes SLA violation degree in cloud service evaluation, so as to make the evaluation more reasonable. Finally, through a set of experiments, we validate the feasibility of our proposal.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Profile5: User Satisfaction Degree With Respect To Kmentioning

confidence: 99%

Section: Cs) = Quality(cs) / (L*m) 14: Return Quality(cs)mentioning

confidence: 99%

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A Sla Violation Degree-Aware Cloud Service Evaluation Approach Based on Historical Records

Qi¹,

Dou²,

Zhang³

et al. 2014

STCC

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“…A bidirectional recommendation approach SD-HCF is introduced in [16], by integrating user-based CF and service-based CF. However, this approach only considers user or service similarity, while omit other important recommendation information.…”

Section: Related Work and Comparison Analysesmentioning

confidence: 99%

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

Zhou

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYWith the ever-increasing number of web services registered in service communities, many users are apt to find their interested web services through various recommendation techniques, e.g., Collaborative Filtering (i.e., CF)-based recommendation. Generally, CF-based recommendation approaches can work well, when a target user has similar friends or the target services (i.e., services preferred by the target user) have similar services. However, when the available user-service rating data is very sparse, it is possible that a target user has no similar friends and the target services have no similar services; in this situation, traditional CF-based recommendation approaches fail to generate a satisfying recommendation result. In view of this challenge, we combine Social Balance Theory (abbreviated as SBT; e.g., "enemy's enemy is a friend" rule) and CF to put forward a novel data-sparsity tolerant recommendation approach Ser Rec S BT +CF . During the recommendation process, a pruning strategy is adopted to decrease the searching space and improve the recommendation efficiency. Finally, through a set of experiments deployed on a real web service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy, recall and efficiency. The experiment results show that our proposed Ser Rec S BT +CF approach outperforms other up-to-date approaches.

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“…The basic idea of hybrid techniques is to combine two or more techniques to benefit from the features in each technique [27]. Due to the disadvantages of some approaches, hybrid techniques have been used to provide a partial solution for addressing such drawback.…”

Section: Related Workmentioning

confidence: 99%

PLMwsp: Probabilistic Latent Model for Web Service QoS Prediction

Madi

Sheng

Yao

et al. 2016

2016 IEEE International Conference on Web Services (ICWS)

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Abstract-With the unprecedented and dramatic development of Web services in recent years, designing novel approaches for efficient Web service prediction has become of paramount importance. Quality of Service (QoS) plays a critical role in Web service recommendation. However determining QoS values of Web services is still a challenging task. For example, some QoS properties (e.g., response time, throughput) may hold different values for different users. In this paper, we describe how to develop a novel approach, PLMwsp, based on a probabilistic latent model, to predict effectively the QoS values of Web services. A Web service prediction has been developed, and experiments have been conducted to show the efficacy of our approach.

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Hybrid Collaborative Filtering algorithm for bidirectional Web service recommendation

Cited by 87 publications

References 32 publications

A Sla Violation Degree-Aware Cloud Service Evaluation Approach Based on Historical Records

A Sla Violation Degree-Aware Cloud Service Evaluation Approach Based on Historical Records

Data-Sparsity Tolerant Web Service Recommendation Approach Based on Improved Collaborative Filtering

PLMwsp: Probabilistic Latent Model for Web Service QoS Prediction

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