Novel Clustering-Based Web Service Recommendation Framework

Author:

Pandharbale Priya Bhaskar1ORCID,Mohanty Sachi Nandan2ORCID,Jagadev Alok Kumar1

Affiliation:

1. School of Computer Engineering, KIIT University (Deemed), Bhubaneswar, India

2. Department of Computer Sc & Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, India

Abstract

Normally web services are classified originate in on the quality of service, wherever the term quality is not absolute and it is a relative term. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, availability, etc. However, the limitation of these methods is that they are producing similar web services in recommendation lists some times. To address this research problem, the novel improved the Clustering-based web service recommendation method is proposed in this project. This approach is mainly dealing to produce diversity in the results of web service recommendation. In this method, functional interest, QoS preference, and diversity features are combined to produce the unique recommendation list of web services to end-users. To produce the unique recommendation results, we proposed a vary web service classify order that is clustering-based on web services' functional relevance such as non-useful pertinence, recorded client intrigue importance, potential client intrigue significance, etc.

Publisher

IGI Global

Subject

General Medicine

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SPCM: A Machine Learning Approach for Sentiment-Based Stock Recommendation System;IEEE Access;2024

2. Joint Content Caching and Recommendation in Opportunistic Mobile Networks Through Deep Reinforcement Learning and Broad Learning;IEEE Transactions on Services Computing;2023-07-01

3. Movie Recommendation Using Content-Based and Collaborative Filtering Approach;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2023

4. Deep auto‐encoder based clustering algorithm for graph‐based web page recommendation system;Concurrency and Computation: Practice and Experience;2022-11-22

5. QoS-Aware Web Services Recommendations Using Dynamic Clustering Algorithms;International Journal of Information System Modeling and Design;2022-09-02

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3