Location Based Service Recommendation System Using Hierarchy Clustering Techniques

Author(s):  
Ravi kumar ◽  
◽  
Kali raj
Author(s):  
Zhuang Shao ◽  
Zhikui Chen ◽  
Xiaodi Huang

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.


Author(s):  
Seung-Min Han ◽  
Mohammad Mehedi Hassan ◽  
Chang-Woo Yoon ◽  
Hyun-Woo Lee ◽  
Eui-Nam Huh

Author(s):  
Amar Khelloufi ◽  
Huansheng Ning ◽  
Sahraoui Dhelim ◽  
Tie Qiu ◽  
Jianhua Ma ◽  
...  

2019 ◽  
Vol 16 (1) ◽  
pp. 65-79
Author(s):  
Abdullah Alawadhi ◽  
Paul E. Byrnes

ABSTRACT Selecting prospective programs in higher education can be a problematic and inefficient task for applicants. In particular, one of the most significant challenges entails locating a specific subset of programs likely to be a good fit. In this paper, clustering techniques are employed in evaluating a specifically created data set of AACSB-accredited doctoral programs in accounting so as to aggregate them by type. In so doing, one is then better positioned to identify which schools best align with his/her relevant characteristics and objectives, thus gaining insight concerning the most appropriate subset of schools to initially consider for application purposes. This approach provides meaningful differentiation between the various program types, offers a means for improving productivity relative to the university application process, and demonstrates promise as an initial foundation for eventual construction of a program recommendation system for use in ostensibly any program application initiative within the higher education domain.


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