Web Service QoS Prediction via Collaborative Filtering: A Survey
| Autor: | Mingdong Tang, Fenfang Xie, Zibin Zheng, Michael R. Lyu, Li Xiaoli |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Service (business)
Information Systems and Management Computer Networks and Communications Computer science media_common.quotation_subject Quality of service 020207 software engineering 02 engineering and technology Recommender system computer.software_genre Adaptability Computer Science Applications World Wide Web Hardware and Architecture Credibility 0202 electrical engineering electronic engineering information engineering Collaborative filtering Contextual information 020201 artificial intelligence & image processing Web service computer media_common |
| Zdroj: | IEEE Transactions on Services Computing. 15:2455-2472 |
| ISSN: | 2372-0204 |
| DOI: | 10.1109/tsc.2020.2995571 |
| Popis: | With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions. |
| Databáze: | OpenAIRE |
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