Personalized Web Service Ranking via User Group Combining Association Rule

Rong, Wenge; Liu, Kecheng; Liang, Lin

doi:10.1109/icws.2009.113

Cited by 61 publications

(33 citation statements)

References 21 publications

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“…A hybrid algorithm was proposed that enhances the user-based approach by integrating item-based CF to achieve better QoS prediction accuracy [20]. A number of similar algorithms have thereafter been developed that leverage other information, such as users' locations [4], [5], invocation frequencies of services [13], and query histories of users [19] to improve the quality of service recommendation. Both user and item based approaches follow the neighborhood centric strategy in CF, which explores the local neighborhood to identify similar users or for recommendation.…”

Section: Related Workmentioning

confidence: 99%

Trace Norm Regularized Matrix Factorization for Service Recommendation

Zheng

Wang

2013

2013 IEEE 20th International Conference on Web Services

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We present in this paper a novel QoS prediction approach to tackle service recommendation, which is to recommend services with the best QoS to users. QoS prediction exploits available QoS information to estimate users' QoS experience from previously unknown services. In this regard, it can be modeled as a general matrix completion problem, which is to recover a large QoS matrix from a small subset of QoS entires. The infinite number of ways to complete an arbitrary QoS matrix makes the problem extremely ill posed. The highly sparse QoS data further complicates the challenges. Nonetheless, real-world QoS data exhibits two key features, which can be leveraged for accurate QoS predictions, leading to high-quality service recommendations. First, QoS delivery can be significantly affected by a small number dominant factors in the service environment (e.g., communication link and user-service distance). Hence, it is natural to assume that the QoS matrix has a low-rank or approximately lowrank structure. Second, users (or services) that share common environmental factors are expected to receive (or deliver) similar QoS and hence can be grouped together.The proposed approach seamlessly amalgamates these two features into a unified objective function and employs an effective iterative algorithm to approach the optimal completion of an arbitrary QoS matrix. We conduct a set of experiments on real-world QoS data to demonstrate the effectiveness of the proposed algorithm.

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Section: Related Workmentioning

confidence: 99%

Trace Norm Regularized Matrix Factorization for Service Recommendation

Zheng

Wang

2013

2013 IEEE 20th International Conference on Web Services

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“…Similarly, Zhou et al [55] modelled services, attributes and the associated entities using a heterogeneous service network, and then employed random walks and cluster matching to generate the ranking. Personalized knowledge can also be used to group users with similar interests together in order to generate an appropriate service ranking [56,57]. Although the above methods can offer some improvement in the ranking accuracy, they may be less practical when the additional semantic information or personalized knowledge is unavailable.…”

Section: Related Workmentioning

confidence: 99%

Search-based QoS ranking prediction for web services in cloud environments

Mao

Chen

Towey

et al. 2015

Future Generation Computer Systems

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“…Nowadays, there is an abundance of real-life applications of recommender systems in the Web, which helps users to deal with information overload in the Internet. The application domains of recommendation range from commercial products such as books, literatures, CDs, TV programs and movies, to recommendation of more complex items such as scientific workflow, quality methods and instruments [12,17]. Many researchers have investigated effective techniques for recommendation.…”

Section: Related Workmentioning

confidence: 99%

Interest-Driven Web Service Recommendation Based on MFI-7

Zhang

Wang

et al. 2013

2013 IEEE International Conference on Services Computing

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Composing and reusing services is the main advantage of Service Oriented Software Engineering (SOSE). Faced with the large amount of Web services which are available on World Wide Web, how to select and recommend suitable Web services becomes a key issue in service computing. The most popular service recommendation technique is QoS-based Collaborate Filtering (CF) with user-service QoS matrix. However, it cannot well capture the new interests of users and handle the cold start problem. In this paper we propose an interest-driven recommendation approach which leverage User Interest Profile (UIP) to represent users' interests. UIP is generated in accordance with an ISO standard named as MFI-7. The similarity between UIPs is used to produceusers' nearest neighbors. The Top-K recommendations will be generated from these neighbors' mostly-used services. To show the effectiveness of our approach, a developed Web service registry and repository platform is used as a testbed to produce preliminary evaluation results.

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Personalized Web Service Ranking via User Group Combining Association Rule

Cited by 61 publications

References 21 publications

Trace Norm Regularized Matrix Factorization for Service Recommendation

Trace Norm Regularized Matrix Factorization for Service Recommendation

Search-based QoS ranking prediction for web services in cloud environments

Interest-Driven Web Service Recommendation Based on MFI-7

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