Efficient web service QoS prediction using local neighborhood matrix factorization

Lo, Wei; Yin, Jianwei; Liu, Ying; Wu, Zhaohui

doi:10.1016/j.engappai.2014.10.010

Cited by 53 publications

(33 citation statements)

References 23 publications

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“…Recently, modern RSs emerged, which improve their recommendations accuracy by using context‐aware, semantic, and other approaches. Context‐aware recommenders use contextual information such as geographical location and time to improve the recommendation accuracy. Semantic RSs use a knowledge base usually defined as a concept diagram or ontology to improve the accuracy of the recommendations …”

Section: Related Workmentioning

confidence: 99%

Enhanced similarity measure for personalized cloud services recommendation

Afify

Moawad²,

Badr³

et al. 2016

Concurrency and Computation

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Summary Cloud users are overwhelmed with great numbers of cloud services. Service recommender systems evaluate the services that provide same functionalities according to the user requirements. A key enabler to accurate recommendation in recommender systems is the appropriate determination of similar users. This paper contributes to the personalized cloud services recommendation area. In specific, we introduce a user‐based similarity measure that integrates relevant similarity aspects: user demographic information, service ratings, and user interest. The proposed similarity measure is used in a hybrid collaborative filtering (CF) approach that leverages the advantages of both model‐ and memory‐based approaches to improve the recommendation process. Experimental evaluation on real‐world services data set shows that the proposed approach outperforms other CF approaches in respect of the prediction accuracy and recommendation time while maintaining better or same coverage.

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Section: Related Workmentioning

confidence: 99%

Enhanced similarity measure for personalized cloud services recommendation

Afify

Moawad²,

Badr³

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…In this section, we focus on the hybrid approaches to which our proposed system belongs. Hybrid approaches involve: (1) combination of CF and CB , (2) combination of CF with demographic filtering , (3) combination of CF with semantics , (4) combination of CF with time and (5) others .…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this section, we focus on the hybrid approaches to which our proposed system belongs. Hybrid approaches involve: (1) combination of CF and CB [4][5][6], (2) combination of CF with demographic filtering [7][8][9][10][11], (3) combination of CF with semantics [12], (4) combination of CF with time [13] and (5) others [14]. First, hybrid approaches that combine CF and CB [4][5][6] exploit the merits of both approaches while avoiding their problems.…”

Section: Introductionmentioning

confidence: 99%

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A personalized recommender system for SaaS services

Afify

Moawad

Badr

et al. 2016

Concurrency and Computation

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Summary In this paper, we propose the Software‐as‐a‐Service (SaaS) Recommender (SaaSRec), a personalized reputation‐based QoS‐aware recommender system (RS) for SaaS services. SaaSRec semantically processes user requests in order to find business‐oriented matching services, which are then filtered to satisfy the user QoS requirements and service characteristics. Subsequently, hybrid filtering is utilized to validate the services set on the basis of services metadata, reputation and user interests. Finally, the recommended set of services is ranked using a unique combination of factors: Relevance to user profile, service reputations and service cost. Moreover, we propose a new method for calculating the service reputation from the objective time‐weighted user feedbacks. SaaSRec addresses many challenges faced by the generic RSs: User cold‐start problem, limited content analysis and low performance. In respect of service RSs, SaaSRec tackles the disregarding of relevant factors to services recommendation: Cloud service characteristics, user physical location, service reputation and user interests. Moreover, SaaSRec provides a hybrid justification for the recommended services to increase the user's acceptance. Experimental evaluation against a real‐world services dataset has been carried out, and the results show that the proposed recommendation approach surpasses other collaborative filtering‐based recommendation approaches in respect of both precision and recall. This performance improvement was verified using different matrix density levels and number of recommendations. Copyright © 2016 John Wiley & Sons, Ltd.

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“…The model-based method usually predefines a domain model for a given system, and then trains the model by existing training datasets for further QoS prediction. This kind of method can incorporate special domain knowledge and multiform artificial intelligence techniques to achieve higher prediction accuracy [13,14], but meanwhile has drawbacks of high complexity and limited universality.…”

Section: Introductionmentioning

confidence: 99%

Response Time Prediction of IoT Service Based on Time Similarity

Yang¹,

Zhang²

2017

Journal of Computing Science and Engineering

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In the field of Internet of Things (IoT), smarter embedded devices offer functions via web services. The Quality-of-Service (QoS) prediction is a key measure that guarantees successful IoT service applications. In this study, a collaborative filtering method is presented for predicting response time of IoT service due to time-awareness characteristics of IoT. First, a calculation method of service response time similarity between different users is proposed. Then, to improve prediction accuracy, initial similarity values are adjusted and similar neighbors are selected by a similarity threshold. Finally, via a densified user-item matrix, service response time is predicted by collaborative filtering for current active users. The presented method is validated by experiments on a real web service QoS dataset. Experimental results indicate that better prediction accuracy can be achieved with the presented method.

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Efficient web service QoS prediction using local neighborhood matrix factorization

Cited by 53 publications

References 23 publications

Enhanced similarity measure for personalized cloud services recommendation

Enhanced similarity measure for personalized cloud services recommendation

A personalized recommender system for SaaS services

Response Time Prediction of IoT Service Based on Time Similarity

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