scholarly journals From Reputation Perspective: A Hybrid Matrix Factorization for QoS Prediction in Location-Aware Mobile Service Recommendation System

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Shun Li ◽  
Junhao Wen ◽  
Xibin Wang
Keyword(s):  
Matrix Factorization ◽  
Recommendation System ◽  
Mobile Services ◽  
End Users ◽  
Mobile Service ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Hybrid Matrix ◽  
Location Aware ◽  
The Impact

With the great development of mobile services, the Quality of Services (QoS) becomes an essential factor to meet end users’ personalized requirement on the nonfunctional performance of mobile services. However, most of the QoS values in real cases are unattainable because a service user would only invoke some specific mobile services. Therefore, how to predict the missing QoS values and recommend high-quality services to end users becomes a significant challenge in mobile service recommendation research. Previous QoS prediction researches demonstrate that the nonfunctional performance of mobile services is closely related to users’ location information. However, most location-aware QoS prediction methods ignore the premise that the obtainable QoS values observed by different users in same location region would probably be untrustworthy, which will lead to inaccurate and unreliable prediction results. To make credible location-aware QoS prediction, we propose a hybrid matrix factorization method integrated location and reputation information (LRMF) to predict the unattainable QoS values. Our approach firstly cluster users into different locational region based on their geographical distribution, and then we compute users’ reputation to identify untrustworthy users in every locational region. Finally, the unknown QoS values can be predicted by integrating locational cluster information and users’ reputation into a hybrid matrix factorization model. Comprehensive experiments are conducted on a public QoS dataset which contains sufficient real-world service invocation records. The evaluation results indicate that our LRMF method can effectively reduce the impact of unreliable users on QoS prediction and make credible mobile service recommendation.

A Mobile Service Recommendation System Using Multi-Criteria Ratings

2010 ◽  
Vol 2 (4) ◽  
pp. 30-40 ◽  
Author(s):  
Zhuang Shao ◽  
Zhikui Chen ◽  
Xiaodi Huang
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Mobile Services ◽  
Rank Aggregation ◽  
Mobile Service ◽  
Service Recommendation ◽  
Service Creation ◽  
Multi Agent ◽  
Individual Criterion ◽  
Service Ranking

With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers’ demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.

scholarly journals Collaborative QoS Prediction for Mobile Service with Data Filtering and SlopeOne Model

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yuyu Yin ◽  
Wenting Xu ◽  
Yueshen Xu ◽  
He Li ◽  
Lifeng Yu
Keyword(s):  
Mobile Applications ◽  
Mobile Services ◽  
Mobile Service ◽  
Ensemble Model ◽  
Data Filtering ◽  
Service Recommendation ◽  
Qos Prediction ◽  
High Data ◽  
Filtering Technique

The mobile service is a widely used carrier for mobile applications. With the increase of the number of mobile services, for service recommendation and selection, the nonfunctional properties (also known as quality of service, QoS) become increasingly important. However, in many cases, the number of mobile services invoked by a user is quite limited, which leads to the large number of missing QoS values. In recent years, many prediction algorithms, such as algorithms extended from collaborative filtering (CF), are proposed to predict QoS values. However, the ideas of most existing algorithms are borrowed from the recommender system community, not specific for mobile service. In this paper, we first propose a data filtering-extended SlopeOne model (filtering-based CF), which is based on the characteristics of a mobile service and considers the relation with location. Also, using the data filtering technique in FB-CF and matrix factorization (MF), this paper proposes another model FB-MF (filtering-based MF). We also build an ensemble model, which combines the prediction results of FB-CF model and FB-MF model. We conduct sufficient experiments, and the experimental results demonstrate that our models outperform all compared methods and achieve good results in high data sparsity scenario.

A Mobile Service Recommendation System Using Multi-Criteria Ratings

2012 ◽  
pp. 255-266
Author(s):  
Zhuang Shao ◽  
Zhikui Chen ◽  
Xiaodi Huang
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Mobile Services ◽  
Rank Aggregation ◽  
Mobile Service ◽  
Service Recommendation ◽  
Service Creation ◽  
Multi Agent ◽  
Individual Criterion ◽  
Service Ranking

With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers’ demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.

uRun

2013 ◽  
pp. 314-334
Author(s):  
Alexandra Chapko ◽  
Andreas Emrich ◽  
Stephan Flake ◽  
Frank Golatowski ◽  
Marc Gräßle ◽  
...  
Keyword(s):  
Mobile Health ◽  
Mobile Device ◽  
Web Applications ◽  
Mobile Services ◽  
End Users ◽  
Mobile Service ◽  
Web Based ◽  
Access Device ◽  
Service Creation ◽  
Health And Fitness

This article presents a framework which enables end users to create small, sharply focused mobile services directly on a mobile device. By this, end users are no longer only consumers of mobile services; they also become producers and providers of mobile services. The domain of mobile health and fitness applications has been chosen to demonstrate the feasibility of the approach. The article presents the underlying platform for easy creation of mobile services and describes the implementation of a Web-based editor for easy mobile service creation as well as our solution to access device capabilities out of Web applications.

QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection

2016 ◽  
Vol 26 (04) ◽  
pp. 611-632 ◽  
Author(s):  
Yuyu Yin ◽  
Song Aihua ◽  
Gao Min ◽  
Xu Yueshen ◽  
Wang Shuoping
Keyword(s):  
Web Service ◽  
Real World ◽  
Missing Values ◽  
Prediction Models ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Network Location ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Neighbor Selection

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.

Privacy Control Requirements for Context-Aware Mobile Services

2011 ◽  
pp. 151-166 ◽  
Author(s):  
Amr Ali Eldin ◽  
Zoran Stojanovic
Keyword(s):  
Web Services ◽  
Private Information ◽  
Mobile Services ◽  
End Users ◽  
Mobile Service ◽  
Context Aware ◽  
University Campus ◽  
Mobile Telecommunications ◽  
Computing Paradigm ◽  
Service Models

With the rapid developments of mobile telecommunications technology over the last two decades, a new computing paradigm known as ‘anywhere and anytime’ or ‘ubiquitous’ computing has evolved. Consequently, attention has been given not only to extending current Web services and mobile service models and architectures, but increasingly also to make these services context-aware. Privacy represents one of the hot topics that has questioned the success of these services. In this chapter, we discuss the different requirements of privacy control in context-aware services architectures. Further, we present the different functionalities needed to facilitate this control. The main objective of this control is to help end users make consent decisions regarding their private information collection under conditions of uncertainty. The proposed functionalities have been prototyped and integrated in a UMTS locationbased mobile services testbed platform on a university campus. Users have experienced the services in real time. A survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’ collected response on the privacy functionality was positive in most cases. Additionally, results obtained reflected the feasibility and usability of this approach.

scholarly journals Situational reasoning for task-oriented mobile service recommendation

2008 ◽  
Vol 23 (1) ◽  
pp. 7-19 ◽  
Author(s):  
MARKO LUTHER ◽  
YUSUKE FUKAZAWA ◽  
MATTHIAS WAGNER ◽  
SHOJI KURAKAKE
Keyword(s):  
Recommendation System ◽  
Mobile Internet ◽  
Mobile Service ◽  
Navigation Systems ◽  
Context Aware ◽  
Service Recommendation ◽  
Multiple Sources ◽  
Best Fitting ◽  
The Many ◽  
Task Oriented

AbstractWe study the case of integrating situational reasoning into a mobile service recommendation system. Since mobile Internet services are rapidly proliferating, finding and using appropriate services require profound service descriptions. As a consequence, for average mobile users it is nowadays virtually impossible to find the most appropriate service among the many offered. To overcome these difficulties, task navigation systems have been proposed to guide users towards best-fitting services. Our goal is to improve the user experience of such task navigation systems making them context-aware (i.e. to optimize service navigation by taking the user's situation into account). We propose the integration of a situational reasoning engine that applies classification-based inference to qualitative context elements, gathered from multiple sources and represented using ontologies. The extended task navigator enables the delivery of situation-aware recommendations in a proactive way. Initial experiments with the extended system indicate a considerable improvement of the navigator's usability.

scholarly journals Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

CONVERTER ◽  
2021 ◽  
pp. 583-589
Author(s):  
Li Ziman
Keyword(s):  
Web Services ◽  
Web Service ◽  
Autonomous System ◽  
Recommendation System ◽  
Service Recommendation ◽  
Filtering Method ◽  
Location Aware ◽  
The Face ◽  
Location Clustering ◽  
User Location

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

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