Proceedings ArticleDOI
A Non Functional Properties Based Web Service Recommender System
Sunita Tiwari,Saroj Kaushik +1 more
- pp 1-4
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TLDR
This work has proposed a personalized web service recommender system that will be very useful to the user in finding web service matching his/her needs and takes advantages of collaborative filtering based, content based and knowledge based approaches and minimize there individual limitations.Abstract:Â
Web services provide a promising solution to an age old need of fast and flexible information sharing among people and businesses. Selection of web service has become a tedious job because of the increasing number of service providers providing services with similar functionality. Service registries are becoming very large preventing users from discovering desired service. Sometimes service users may not be aware of services that can be most beneficial to them. Therefore, a framework for selection of web service that can meet the user's specific requirements is needed. In this work, we have proposed a personalized web service recommender system that will be very useful to the user in finding web service matching his/her needs. A recommender system helps product/service user to deal with information overload and provides personalized recommendation to them. There have been a few web service recommendation system in past, but most of them are either content based or collaborative filtering based recommendation. But all of these approaches have their own limitations. In our work we have proposed a web service recommender system based on hybrid technique which takes advantages of collaborative filtering based, content based and knowledge based approaches and minimize there individual limitations.read more
Citations
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Proceedings ArticleDOI
QoE Estimation for Web Service Selection Using a Fuzzy-Rough Hybrid Expert System
TL;DR: A novel method based on a fuzzy-rough hybrid expert system for estimating QoE of web services for web service selection is proposed and the simulation results show that the estimated web quality from the system has a high correlation with the subjectiveQoE obtained from the participants in controlled tests.
Book ChapterDOI
Crowdsourcing Based Fuzzy Information Enrichment of Tourist Spot Recommender Systems
Sunita Tiwari,Saroj Kaushik +1 more
TL;DR: F fuzzy inference is proposed to compute a new score/rank, with each recommended spot, for each spot to be recommender based on the recommender's rank and current context.
Book ChapterDOI
Implementation of Adaptive Framework and WS Ontology for Improving QoS in Recommendation of WS
TL;DR: A framework for recommendation of personalized WS coupled with the quality optimization, using the quality features available in WS Ontology is implemented, which helps users to acquire the best recommendation by consuming the contextual information and the quality of WS.
Book ChapterDOI
Location Aware Personalized News Recommender System Based on Twitter Popularity
TL;DR: This work proposes an approach to recommend the personalized news to the users based on their individual preferences and believes that the interest of the user, popularity of article and other attributes of news are implicitly fuzzy in nature and therefore this is exploited for generating the recommendation score for articles to be recommended.
Book ChapterDOI
Learning User Preferences for Recommender System Using YouTube Videos Tags
TL;DR: This work proposes an approach to learn implicit user preferences by making use of YouTube Video Tags, which is generic and may be used for a wide variety of domains of recommender systems.
References
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Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
TL;DR: This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches.
Journal ArticleDOI
Hybrid Recommender Systems: Survey and Experiments
TL;DR: This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
Proceedings ArticleDOI
QoS computation and policing in dynamic web service selection
TL;DR: This paper presented an open, fair and dynamic QoS computation model for web services selection through implementation of and experimentation with a QoS registry in a hypothetical phone service provisioning market place application.
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