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Proceedings ArticleDOI: 10.1109/CISE.2010.5676953

A Non Functional Properties Based Web Service Recommender System

30 Dec 2010-pp 1-4
Abstract: 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. more

Topics: Web service (70%), Service provider (65%), Service design (65%) more

Proceedings ArticleDOI: 10.1109/AINA.2014.77
13 May 2014-
Abstract: With the proliferation of web services on the Inter-net, it has become important for service providers to select the best services for their clients in accordance to their functional and non-functional requirements. Generally, QoS parameters are used to select the most performing web services, however, these parameters do not necessarily reflect the user's satisfaction. Therefore, it is necessary to estimate the quality of web services on the basis of user satisfaction, i.e., Quality of Experience(QoE). In this paper, we propose a novel method based on a fuzzy-rough hybrid expert system for estimating QoE of web services for web service selection. It also presents how different QoS parameters impact the QoE of web services. For this, we conducted subjective tests in controlled environment with real users to correlate QoS parameters to subjective QoE. Based on this subjective test, we derive membership functions and inference rules for the fuzzy system. Membership functions are derived using a probabilistic approach and inference rules are generated using Rough Set Theory (RST). We evaluated our system in a simulated environment in MATLAB. The simulation results show that the estimated web quality from our system has a high correlation with the subjective QoE obtained from the participants in controlled tests. more

Topics: Web service (61%), Quality of experience (53%), Service provider (51%) more

19 Citations

Book ChapterDOI: 10.1007/978-3-319-21410-8_43
Sunita Tiwari1, Saroj Kaushik2Institutions (2)
22 Jun 2015-
Abstract: Tourist Spot Recommender Systems TSRS help users to find the interesting locations/spots in vicinity based on their preferences. Enriching the list of recommended spots with contextual information such as right time to visit, weather conditions, traffic condition, right mode of transport, crowdedness, security alerts etc. may further add value to the systems. This paper proposes the concept of information enrichment for a tourist spot recommender system. Proposed system works in collaboration with a Tourist Spot Recommender System, takes the list of spots to be recommended to the current user and collects the current contextual information for those spots. A new score/rank is computed for each spot to be recommender based on the recommender's rank and current context and sent back to the user. Contextual information may be collected by several techniques such as sensors, collaborative tagging folksonomy, crowdsourcing etc. This paper proposes an approach for information enrichment using just in time location aware crowdsourcing. Location aware crowdsourcing is used to get current contextual information about a spot from the crowd currently available at that spot. Most of the contextual parameters such as traffic conditions, weather conditions, crowdedness etc. are fuzzy in nature and therefore, fuzzy inference is proposed to compute a new score/rank, with each recommended spot. The proposed system may be used with any spot recommender system, however, in this work a personalized tourist spot recommender system is considered as a case for study and evaluation. A prototype system has been implemented and is evaluated by 104 real users. more

Topics: Recommender system (58%), Crowdsourcing (55%), Folksonomy (52%)

9 Citations

Book ChapterDOI: 10.1007/978-3-319-47952-1_30
21 Sep 2016-
Abstract: With the advent of more users accessing internet for information retrieval, researchers are more focused in creating system for recommendation of web service(WS) which minimize the complexity of selection process and optimize the quality of recommendation. This paper implements a framework for recommendation of personalized WS coupled with the quality optimization, using the quality features available in WS Ontology. It helps users to acquire the best recommendation by consuming the contextual information and the quality of WS. Adaptive framework performs i) the retrieval of context information ii) calculation of similarity between users preferences and WS features, similarity between preferred WS with other WS specifications iii) collaboration of web service ratings provided by current user and other users. Finally, WS quality features are considered for computing the Quality of Service. The turnout of recommendation reveals the selection of highly reliable web services, as credibility is used for QoS predication. more

7 Citations

Book ChapterDOI: 10.1007/978-3-319-95171-3_51
02 May 2018-
Abstract: The mobile and handheld devices have become an indispensable part of life in this era of technological advancement. Further, the ubiquity of location acquisition technologies like global positioning system (GPS) has opened the new avenues for location aware applications for mobile devices. Reading online news is becoming increasingly popular way to gather information from news sources around the globe. Users can search and read the news of their preference wherever they want. The news preferences of individuals are influenced by several factors including the geographical contexts and the recent trends on social media. In this work we propose an approach to recommend the personalized news to the users based on their individual preferences. The model for user preferences are learned implicitly for individual users. Also, the popularity of trending articles floating around the twitter are exploited to provide news interesting recommendations to the user. We believe that the interest of the user, popularity of article and other attributes of news are implicitly fuzzy in nature and therefore we propose to exploit this for generating the recommendation score for articles to be recommended. The prototype is developed for testing and evaluation of proposed approach and the results of the evaluation are motivating. more

Topics: Social media (52%), Popularity (51%), Recommender system (51%)

5 Citations

Book ChapterDOI: 10.1007/978-3-319-95171-3_36
Sunita Tiwari1, Abhishek Jain1, Prakhar Kothari1, Rahul Upadhyay1  +1 moreInstitutions (1)
02 May 2018-
Abstract: Recommender systems have become essential in several domains to deal with the problem of information overload. Collaborative filtering is one of the most popularly used paradigm of recommender systems for over a decade. The personalized recommender systems use past preference history of the users to make future recommendations for them. The cold start problem of recommender system concerns with the personalized recommendation to the users having no or few past history. In this work we propose an approach to learn implicit user preferences by making use of YouTube Video Tags. The profile of a new user is created from his/her preferences in watching the YouTube videos. This profile is generic and may be used for a wide variety of domains of recommender systems. In this work we have used it for a biography recommender system. However this may be used for several other types of recommender system. more

Topics: Recommender system (66%), Collaborative filtering (61%), Cold start (61%) more

3 Citations


Journal ArticleDOI: 10.1109/21.256541
Jyh-Shing Roger Jang1Institutions (1)
01 May 1993-
Abstract: 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. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. > more

Topics: Adaptive neuro fuzzy inference system (79%), Neuro-fuzzy (65%), Fuzzy control system (63%) more

13,738 Citations

Journal ArticleDOI: 10.1109/TKDE.2005.99
Abstract: 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. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multicriteria ratings, and a provision of more flexible and less intrusive types of recommendations. more

Topics: Recommender system (59%), Collaborative filtering (57%), Cold start (55%) more

9,202 Citations

Journal ArticleDOI: 10.1023/A:1021240730564
Robin Burke1Institutions (1)
Abstract: Recommender systems represent user preferences for the purpose of suggesting items to purchase or examine They have become fundamental applications in electronic commerce and information access, providing suggestions that effectively prune large information spaces so that users are directed toward those items that best meet their needs and preferences A variety of techniques have been proposed for performing recommendation, including content-based, collaborative, knowledge-based and other techniques To improve performance, these methods have sometimes been combined in hybrid recommenders 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 Further, we show that semantic ratings obtained from the knowledge-based part of the system enhance the effectiveness of collaborative filtering more

Topics: Recommender system (71%), Collaborative filtering (67%), Information filtering system (64%) more

3,578 Citations

Open access
01 Jan 1993-

1,790 Citations

Proceedings ArticleDOI: 10.1145/1013367.1013379
Yutu Liu1, Anne H. H. Ngu1, Liang Z. Zeng2Institutions (2)
19 May 2004-
Abstract: The emerging Service-Oriented Computing (SOC) paradigm promises to enable businesses and organizations to collaborate in an unprecedented way by means of standard web services. To support rapid and dynamic composition of services in this paradigm, web services that meet requesters' functional requirements must be able to be located and bounded dynamically from a large and constantly changing number of service providers based on their Quality of Service (QoS). In order to enable quality-driven web service selection, we need an open, fair, dynamic and secure framework to evaluate the QoS of a vast number of web services. The fair computation and enforcing of QoS of web services should have minimal overhead but yet able to achieve sufficient trust by both service requesters and providers. In this paper, we presented our 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. more

Topics: Mobile QoS (69%), Web service (66%), WS-Policy (66%) more

954 Citations

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