CAMER: A Context-Aware Mobile Service Recommendation System

2016 ◽  
Author(s):  
Zhengzhe Xiang ◽  
Shuiguang Deng ◽  
Songguo Liu ◽  
Bin Cao ◽  
Jianwei Yin
Keyword(s):  
Recommendation System ◽  
Mobile Service ◽  
Context Aware ◽  
Service Recommendation

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.

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.

Online role mining for context-aware mobile service recommendation

2013 ◽  
Vol 18 (5) ◽  
pp. 1029-1046 ◽  
Author(s):  
Raymond K. Wong ◽  
Victor W. Chu ◽  
Tianyong Hao
Keyword(s):  
Mobile Service ◽  
Context Aware ◽  
Service Recommendation ◽  
Role Mining

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

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.

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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