An Extended Matrix Factorization Approach for QoS Prediction in Service Selection

Lo, Wei; Yin, Jianwei; Deng, Shuiguang; Lü, Ying; Wu, Zhaohui

doi:10.1109/scc.2012.36

Cited by 89 publications

(64 citation statements)

References 13 publications

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“…It uses behavior to build of line models like Bayesian, that capture the relationship between the item or user relations. Matrix factorization is one of the most popular model-based CF approaches, which was first introduced to address the QoS prediction problem in [8]. Drawbacks:-Inflexible Quality of predictions , Synonyms, Problem and Cold-start problem.…”

Section: Model Based Collaborative Filteringmentioning

confidence: 99%

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A Survey on Personalized Recommendation System for Web Services

2016

IJSR

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we are presenting a survey on the Personalized web service recommendation systems. Web services are integrated software components for the support of interoperable machines interaction over a network, Many different Techniques and algorithms of recommendations are discussed, In this paper we present the recommendation system related most popular techniques with their pros and cons. Collaborative filtering technique is a most popular technique used for the recommendation system. We will present the research related to collaborative filtering has been done. In the end we will show the main challenges of recommender systems.

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Section: Model Based Collaborative Filteringmentioning

confidence: 99%

“…Dong, A. Halevy, J. Madhavan, E. Nemes, and J. Zhang [8] have presented the fundamental alogorithm the Woogle search engine for web services. It makes provisions for similarity search for web services, such as finding the similar web-service procedures.…”

Section: Je Haddad Et Al [15] Address the Issue Of Recommendingmentioning

confidence: 99%

A Survey on Personalized Recommendation System for Web Services

2016

IJSR

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“…Lo et al [24] also combine service similarity and Matrix Factorization in their missing value prediction. Besides serving different purposes, we infer implicit correlations among APIs rather than directly using the explicit API similarity for making recommendations.…”

Section: Related Workmentioning

confidence: 99%

Service Recommendation for Mashup Composition with Implicit Correlation Regularization

Yao

Wang

Sheng

et al. 2015

2015 IEEE International Conference on Web Services

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Abstract-In this paper, we explore service recommendation and selection in the reusable composition context. The goal is to aid developers finding the most appropriate services in their composition tasks. We specifically focus on mashups, a domain that increasingly targets people without sophisticated programming knowledge. We propose a probabilistic matrix factorization approach with implicit correlation regularization to solve this problem. In particular, we advocate that the coinvocation of services in mashups is driven by both explicit textual similarity and implicit correlation of services, and therefore develop a latent variable model to uncover the latent connections between services by analyzing their co-invocation patterns. We crawled a real dataset from ProgrammableWeb, and extensively evaluated the effectiveness of our proposed approach.

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“…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]. Wei lo et al [15] have proposed an extended matrix factorization framework, this model is quite effective and scales to the large dataset.In [16],Chen et al have proposed an enhanced QoS prediction approach, which uses A-cosine equation for similarity calculation to remove the impact of different QoS scale and adds a data smoothing process to improve the prediction accuracy , to predict the missing QoS values. Sergio et al [17] have investigated the Markovian Arrival Processes (MAP) and the related MAP/MAP/1 queueing model as a tool for performance prediction of servers deployed in the cloud.…”

Section: Related Workmentioning

confidence: 99%

“…Zibin's dataset [22], [23] is popularly used in a number of papers [15], [20], this is a real-world dataset which only consists of two types of QoS but not WSDL files. In order to discover the topic information of Web services, we crawl the corresponding WSDL files whose URLs (Uniform Resource Locator) have been given in Zibin's dataset from Internet.…”

Section: Datasetsmentioning

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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An Extended Matrix Factorization Approach for QoS Prediction in Service Selection

Cited by 89 publications

References 13 publications

A Survey on Personalized Recommendation System for Web Services

A Survey on Personalized Recommendation System for Web Services

Service Recommendation for Mashup Composition with Implicit Correlation Regularization

Topic Model based Collaborative QoS Prediction

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