RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation

Chen, Xi; Li, Xudong; Huang, Zicheng; Sun, Hailong

doi:10.1109/icws.2010.27

Cited by 214 publications

(120 citation statements)

References 12 publications

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“…Zheng et al develop a model which enhance the user based Collaborative Filtering by fusing item-based CF [10]. The model-based method permits the system to create clever forecast for the CF algorithms tasks based on some learned models [5,6]. Matrix factorization (MF) is one of the perfect works.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, there are service shoppers WHO antecedently have experience in victimization net services and so will facilitate in selecting services with adequate quality. This principle is extensively used by (collaborative) recommendation and reputation systems [6] [7]. Often net services are adapt not on public use but aim at enabling simple info exchange between a set of partner organizations.…”

Section: Introductionmentioning

confidence: 99%

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QoS Based Approach for Web Service Recommendation

Chame¹,

Deshpande²

2016

IJECS

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Web services are important integrated software components for the support of interoperable machine to machine communication over internet. Web services have been widely used for building services oriented applications in industry in recent years. On internet publically available web services are widely increased in recent years. For the web services users it is very hard to select a proper web services among a large amount of services available on internet. Improper selection of web services may cause many problems (e.g., illsuited performance) to the resulting applications. In this paper we proposed Collaborative Filtering (CF), we propose an innovative CF algorithm for Q-o-S-based web service recommendation. we provide a personalized map for browsing the recommendation results. The map explicitly shows the QoS relationships of the recommended web services as well as the basic structure of the QoS space by using map metaphor such as dots, areas, and spatial arrangement.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

QoS Based Approach for Web Service Recommendation

Chame¹,

Deshpande²

2016

IJECS

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“…Zheng et al [12] have proposed a hybrid approach named WSRec that combines user-based and item-based methods together to predict the QoS values of Web services. Chen et al [13] have propose a region-based hybrid collaborative filtering algorithm to predict the QoS values taking advantage of the great influence of user's location to the accuracy of prediction. Zibin' NIMF (neighborhood integrated matrix factorization) model takes advantage of the past Web service usage experience of service users to predict Web service QoS value for users [14].…”

Section: Related Workmentioning

confidence: 99%

Topic Model based Collaborative QoS Prediction

Wu¹,

Ji²,

Liangand³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:With the increasing development and growth of Web services on the World Wide Web, the demand of appropriate Web service selection approaches are unprecedentedly strong, and Quality-of-Service (QoS) based service computing is becoming an important issue of service-oriented computing. In most of previous works, the QoS values of services to users are all conceived to be known, however, lots of them are unknown in practice application. Recently, lots of literatures aiming at predicting such missing QoS values are published, they all consider the unknown QoS values prediction as a fundamental step for the QoS-based service computing. Looking through existing works, we discover that the online cold-start scenario, in which some new coming Web services haven't been involved even once, hasn't been considered carefully. In this paper, we utilize a collaborative framework by integrating matrix factorization with probabilistic topic model to predict QoS values. Specifically, the basic idea of the proposed approach is collaborative filtering via matrix factorization, while the cold-start problem is handled by employing probabilistic topic model based on WSDL (Web Service Description Language) documents. The experiment are based on two real-world datasets (one contains 100 users and 150 Web services, and the other contains 339 users and 2344 Web services), and the results demonstrate the prediction accuracy of the proposed approach.

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“…Similar users are often grouped according to their IP addresses [15] however using the IP to determine the closeness of the user is not always accurate [2]. To determine the regional information, we use the AS number that is unique to each region within a country.…”

Section: Similar User Computationmentioning

confidence: 99%

Location-Aware Collaborative Filtering for Web Service Recommendations Based on User and Service History

Chelliah¹,

Vivekanandan²

2017

JESTR

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The proliferation of technologies based on web services in the past few years has driven the exponential growth of the number of services available to the user. Due to the large number of candidates, it is difficult for a user to select the service best suited to their needs. Thus it is of paramount importance to devise a strategy to recommend the appropriate service to a given user. Collaborative Filtering (CF) is a widely employed technique to filter relevant data in Web Service Recommendations (WSRs). Although several CF based WSR techniques have been proposed over the past few years, their performance still requires significant improvement.In this paper, we propose a CF approach that leverages the location of the users for the filtering process. This ensures a greater measure of similarity between users to aid in making recommendations. Moreover, we also consider the history of the user and the web service to produce accurate recommendations. This is done by assigning weights to a candidate based on the user or service history to produce a similarity measure allowing the system to accurately determine the preferences of a user and make recommendations accordingly. The results of our proposed method are simulated through a set of comprehensive experiments performed on a real world Web Service dataset that is used to determine its performance.

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RegionKNN: A Scalable Hybrid Collaborative Filtering Algorithm for Personalized Web Service Recommendation

Cited by 214 publications

References 12 publications

QoS Based Approach for Web Service Recommendation

QoS Based Approach for Web Service Recommendation

Topic Model based Collaborative QoS Prediction

Location-Aware Collaborative Filtering for Web Service Recommendations Based on User and Service History

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