Diversified QoS-Centric Service Recommendation for Uncertain QoS Preferences

Liang

et al. 2022

Preprint

Self Cite

With the popularity and widespread adoption of SOA (Service Orient Architecture), the number of Web services has increased exponentially on the Internet. Meanwhile, as the cost of Internet applications decreases, users tend to use online Web services for their daily business and software development needs. Under such a background, recommending desirable Web services to users which meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue owing to the QoS preference is usually hard to provide for users, i.e., QoS preference is uncertain. To solve the problem, some recent works try to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the former QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the diversified preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the user’s service invocation history and provides a Web service recommendation algorithm, named personalized DPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experiment results show that the proposed approach can provide personalized and diversified Web services on the premise of ensuring the overall accuracy of the recommendation results.

Section: Related Workmentioning

confidence: 99%

Section: Qos Accuracy and Qos Diversity Preferencementioning

confidence: 99%

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QoS-Centric Web service Recommendation with Personalized Diversity

Liang

et al. 2022

Preprint

Self Cite

“…As for the QoS-based approaches, they are especially useful for QoS-aware Web service selection, [34][35][36] service composition, [37][38][39] and service recommendation. 40,41 However, QoS information is difficult to collect, and QoS values change over time and are subject to the network environment, QoS-based approaches are not practical. 42 As for the functionality based approaches, there are still some obvious drawbacks: (1) the keyword order and context information between keywords, which are essential to fully characterize services functional semantics.…”

Section: Related Workmentioning

confidence: 99%

“…As for the QoS‐based approaches, they are especially useful for QoS‐aware Web service selection, 34‐36 service composition, 37‐39 and service recommendation 40,41 . However, QoS information is difficult to collect, and QoS values change over time and are subject to the network environment, QoS‐based approaches are not practical 42 .…”

Section: Related Workmentioning

confidence: 99%

Tatt‐BiLSTM: Web service classification with topical attention‐based BiLSTM

Xiao

Concurrency and Computation

et al. 2021

Self Cite

With the rapid growth of the number of Web services on the Internet, how to classify Web services correctly and efficiently become particularly important in service management tasks, such as service discovery, service selection, service ranking, and service recommendation. Existing functionality-based service classification techniques have some drawbacks: (1) the keyword order and context information are not considered; (2) the embedding features of keywords are taken as equal importance to learn the classification model; (3) the topic number is hard to determine manually. Due to these drawbacks, the accuracy of service classification needs to be improved further. At present, deep learning techniques show the strong power in modeling complex and nonlinear function relationship. Thus, to address the problems above, this paper exploits attention mechanism to combine the local implicit state vector of Bidirectional Long Short-Term Memory Network (BiLSTM) and the global hierarchical Dirichlet process (HDP) topic vector, and proposes a Web service classification approach with topical attention-based BiLSTM. Specifically, BiLSTM is used to automatically learn the keyword feature representations of Web services. Then, the topic vectors of Web service documents are obtained with HDP by offline training, and topic attention mechanism is adopted to strengthen the feature representation by discriminating the importance or weight of different keywords in Web service documents. Finally, the enhanced Web service feature representation is used as the input of a softmax neural network layer to perform the classification prediction for Web services. Extensive experiments are conducted to validate the effectiveness of the proposed approach.

Spatial‐temporal aware service composition for production factors under industrial internet

Xie

Concurrency and Computation

et al. 2023

Industrial Internet is a promising technology combining industrial systems with Internet techniques to significantly improve production efficiency and reduce cost by cooperating with intelligent devices. Under industrial internet environment, a production process usually consists of multiple subtasks, and one or more types of product factors are needed to finish a subtask. Thus, the service composition under industrial application is more complex and challenging compared with traditional service composition under the Internet environment. In this article, we model the problem of service composition for production factors under industrial internet as a multiobjective optimization problem. To derive the optimal Pareto service composition plans, we propose a hybrid optimization algorithm, named TLBO-TS, by combining the advantages of teaching-learning-based optimization algorithm and tabu search algorithm.Extensive experiments are conducted to compare with other population-based optimization methods under a real-world ship production process to verify the superiority of our approach.