S-RAP: relevance-aware QoS prediction in web-services and user contexts

With quick advancement in web technology, web-services offered on internet are growing quickly, making it challenging for users to choose a web-service fit to their needs. Recommender systems save users the hassle of going through a range of products by product recommendations through analytical tec...

Full description

Saved in:
Bibliographic Details
Published in:Knowledge and information systems 2022-07, Vol.64 (7), p.1997-2022
Main Authors: Muslim, Hafiz Syed Muhammad, Rubab, Saddaf, Khan, Malik M., Iltaf, Naima, Bashir, Ali Kashif, Javed, Kashif
Format: Article
Language:English
Subjects:
Collaboration
Computer engineering
Computer Science
Data Mining and Knowledge Discovery
Database Management
Information Storage and Retrieval
Information Systems and Communication Service
Information Systems Applications (incl.Internet)
IT in Business
Privacy
Quality of service
Recommender systems
Regular Paper
Reputations
Software
User experience
User needs
Web services
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With quick advancement in web technology, web-services offered on internet are growing quickly, making it challenging for users to choose a web-service fit to their needs. Recommender systems save users the hassle of going through a range of products by product recommendations through analytical techniques on historical data of user experiences of the available items/products. Research efforts provide several methods for web-service recommendation in which QoS-related attributes play primary role such as response-time, throughput, security, privacy and web-service-delivery. Derivable attributes including, user-trustworthiness and web-services reputation in contexts of users and web-services can also affect the QoS prediction. The proposed research focuses on a web-service recommendation model, S-RAP, for QoS prediction based on derivable attributes to predict QoS of a web-service that a user who has not invoked it before would experience. Services-Relevance attribute is proposed in this publication, which emphasizes on employing the historical data and extracting the degree of relevance in the users and web-services context to predict the QoS values for a user. The proposed system produces satisfactorily accurate rating predictions in the experiments evaluated by the Mean Absolute Error and Normalized Mean Absolute Error metrics. The results compared with state-of-the-art models show a relative improvement by 4.0%.
ISSN:0219-1377
0219-3116
DOI:10.1007/s10115-022-01699-0