A Ranking-Oriented Hybrid Approach to QoS-Aware Web Service Recommendation

Chen, Mingming; Ma, Yutao; Hu, Bo; Zhang, Liang-Jie

doi:10.1109/scc.2015.84

Cited by 13 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the TopK, the choice of its range of values is analogous to other research studies. 6,7,10,13,35 In particular, the TopK value is set to 10, 15, and 20 by Wu et al, 6 to 5 and 10 by Zheng et al, 7 from 10 to 50 by Zheng et al, 10 from 5 to 40 by Chen et al, 13 and from 10 to 45 by Chen et al 35 In this article, we used TopK values 10 to 40.…”

Section: Preprocessingmentioning

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

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.

show abstract

Section: Preprocessingmentioning

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%

“…First, hybrid approaches that combine CF and CB exploit the merits of both approaches while avoiding their problems. Authors of proposed semantic recommendation of services via a hybrid approach that combines CF and semantic CB methods.…”

Section: Literature Reviewmentioning

confidence: 99%

“…2. Test Set 2: Users with many (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) feedbacks. It consists of 285 users.…”

Section: Datasetsmentioning

confidence: 99%

“…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. Authors of [4] proposed semantic recommendation of services via a hybrid approach that combines CF and semantic CB methods.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A personalized recommender system for SaaS services

Afify

Moawad

Badr

et al. 2016

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract

QoS-Aware Graph Contrastive Learning for Web Service Recommendation

Choi,

Ryu

2023

2023 30th Asia-Pacific Software Engineering Conference (APSEC)

View full text Add to dashboard Cite

A Ranking-Oriented Hybrid Approach to QoS-Aware Web Service Recommendation

Cited by 13 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

QoS-Aware Graph Contrastive Learning for Web Service Recommendation

Contact Info

Product

Resources

About