QoS-Aware Web Service Recommendation with Reinforced Collaborative Filtering

Zou, Guobing; Jiang, Ming; Niu, Sen; Wu, Hao; Pang, Shengye; Gan, Yanglan

doi:10.1007/978-3-030-03596-9_31

Cited by 32 publications

(29 citation statements)

References 14 publications

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“…For example, in [22], a hybrid random walk approach was adopted in computing the similarities between indirect users or services, and an improved CF model was designed for service recommendation. Zou et al [23] integrated user-intensive and service-intensive CF in a reinforced CF approach and eliminated the interference of the services (or users) dissimilar with the target service (or the target user). In [24]- [26], the authors built a heterogeneous information network (HIN) using various types of information of mashups and services, measured an overall similarity score between mashups based on HIN, and finally, made a rating prediction using the user-based CF.…”

Section: B Cf-based Service Recommendationmentioning

confidence: 99%

A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

Geng

Wang

2021

IEEE Trans. Eng. Manage.

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As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending appropriate component services for developers on demand is a vital issue in the development of mashups. In particular, since a new mashup to develop contains no component services, it is a new "user" to a service recommender system. To address this new "user" cold-start problem, we propose a multiplex interactionoriented service recommendation approach, named MISR, which incorporates three types of interactions between services and mashups into a deep neural network. In this article, we utilize the powerful representation learning abilities provided by deep learning to extract hidden structures and features from various types of interactions between mashups and services. Experiments conducted on a real-world dataset from ProgrammableWeb show that MISR outperforms several state-of-the-art approaches regarding commonly used evaluation metrics.

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Section: B Cf-based Service Recommendationmentioning

confidence: 99%

A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

Geng

Wang

2021

IEEE Trans. Eng. Manage.

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“…Web service combinations with cooperation is an area that some scholars have performed relevant researches. Mao et al [14] and Zou et al [15] considered a cooperative filtering approach to obtain more accurate predictions of web services. Tan et al [16] developed an improved selforganizing neural network web service composition method, and Yao et al [17] combined cooperative filtering with content recommendations.…”

Section: Related Workmentioning

confidence: 99%

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

et al. 2020

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Microservices is a new paradigm in cloud computing that separates traditional monolithic applications into groups of services. These individual services may correlate or cross multi-clouds. Compared to a monolithic architecture, microservices are faster to develop, easier to deploy, and maintain by leveraging modern containers or other lightweight virtualization. To satisfy the requirements of end-users and preferences, appropriate microservices must be selected to compose complicated workflows or processes from within a large space of candidate services. The microservice composition should consider several factors, such as user preference, correlation effects, and fuzziness. Due to this problem being NP-hard, an efficient metaheuristic algorithm to solve large-scale microservice compositions is essential. We describe a microservice composition problem for multi-cloud environments that considers service grouping relations and corresponding correlation effects of the service providers within intra-or inter-clouds. We use the triangular fuzzy number to describe the uncertainty of QoS attributes, the improved fuzzy analytic hierarchy process to calculate multi-attribute QoS, construct fuzzy weights related to user preferences, and transform the multi-optimal problem into a single-optimal problem. We propose a new artificial immune algorithm based on the immune memory clone and clone selection algorithms. We also introduce several optimal strategies and conduct numerical experiments to verify effects and efficiencies. Our proposed method combines the advantages of monoclone, multi-clone, and co-evolution, which are suitable for the large-scale problems addressed in this paper. INDEX TERMS Fuzzy analytic hierarchy process (FAHP), microservice, microservice group, quality of service (QoS), QoS attribute, the parallel cooperative short-term memory injection multi-clone clonal selection algorithm (ParaCoSIMCSA).

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“…According to the existing QoS data learning model, He et al [14] proposed a location-based Hierarchical Matrix Factorization model (HMF), predicting QoS missing values by combining the results of local matrix factorization and global matrix factorization. Zou et al [15] proposed an enhanced collaborative filtering model for QoS based service recommendation. Although matrix factorization can deal with the sparsity problem, it suffers information loss in most cases [16].It can be seen that these methods complete the prediction task by neighborhood information.…”

Section: Related Workmentioning

confidence: 99%

A Two-Tier Service Filtering Model for Web Service QoS Prediction

Zhang

2020

IEEE Access

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QoS-Aware Web Service Recommendation with Reinforced Collaborative Filtering

Cited by 32 publications

References 14 publications

A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

Optimization of Microservice Composition Based on Artificial Immune Algorithm Considering Fuzziness and User Preference

A Two-Tier Service Filtering Model for Web Service QoS Prediction

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