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.

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 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.

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.

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 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.

Study of Personalized Information Filtering System Based on Multi-Agent

2011 ◽  
Vol 267 ◽  
pp. 913-917
Author(s):  
Song Jie Gong
Keyword(s):  
Customer Service ◽  
System Architecture ◽  
Recommendation System ◽  
Information Overload ◽  
Information Filtering ◽  
Functional Modules ◽  
Service Model ◽  
Web Information ◽  
Problem Solving Environment ◽  
Multi Agent

The growth of Web information resources leads the phenomenon of information overload and resources disorientation. In order to adapt to the existing recommendation system dynamically changing needs of e-commerce sites, provide users with more proactive, intelligent adaptive personalized product recommendation service, and achieve truly reflect the "information to look for" service model, in this paper, a personalized information filtering system based on multi-agent is presented. The filtering system architecture, agent parts, workflow design are given too. The system will recommend a variety of functional modules in the system can be constructed to take the initiative to customer service and a problem solving environment in self-run entity. This system allows the application of the traditional recommendation system is more malleable, autonomy, and more suitable for the needs of users in a dynamic, uncertain environment to use.

uRun

2011 ◽  
Vol 2 (4) ◽  
pp. 79-97
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.

scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

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