Context-aware role mining for mobile service recommendation

2012 ◽  
Author(s):  
Jian Wang ◽  
Cheng Zeng ◽  
Chuan He ◽  
Liang Hong ◽  
Liang Zhou ◽  
...  
Keyword(s):  
Mobile Service ◽  
Context Aware ◽  
Service Recommendation ◽  
Role Mining

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 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 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.

Service-Oriented Architectures for Context-Aware Information Retrieval and Access

2010 ◽  
pp. 1549-1560
Author(s):  
Lu Yan
Keyword(s):  
Information Retrieval ◽  
Information Exchange ◽  
Exchange Process ◽  
Information Access ◽  
Mobile Service ◽  
Context Aware ◽  
Service Oriented Architectures ◽  
Information Retrieval Systems ◽  
To Come ◽  
Process Context

Humans are quite successful at conveying ideas to each other and retrieving information from interactions appropriately. This is due to many factors: the richness of the language they share, the common understanding of how the world works, and an implicit understanding of everyday situations (Dey & Abowd, 1999). When humans talk with humans, they are able to use implicit situational information (i.e., context) to enhance the information exchange process. Context (Cool & Spink, 2002) plays a vital part in adaptive and personalized information retrieval and access. Unfortunately, computer communications lacks this ability to provide auxiliary context in addition to the substantial content of information. As computers are becoming more and more ubiquitous and mobile, there is a need and possibility to provide information “personalized, any time, and anywhere” (ITU, 2006). In these scenarios, large amounts of information circulate in order to create smart and proactive environments that will significantly enhance both the work and leisure experiences of people. Context-awareness plays an important role to enable personalized information retrieval and access according to the current situation with minimal human intervention. Although context-aware information retrieval systems have been researched for a decade (Korkea-aho, 2000), the rise of mobile and ubiquitous computing put new challenges to issue, and therefore we are motivated to come up with new solutions to achieve non-intrusive, personalized information access on the mobile service platforms and heterogeneous wireless environments.

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.

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