Activity Recommendation Model Using Rank Correlation for Chronic Stress Management

Social Recommendation Using Low-Rank Semidefinite Program

The most critical challenge for the recommendation system is to achieve the high prediction quality on the large scale sparse data contributed by the users. In this paper, we present a novel approach to the social recommendation problem, which takes the advantage of the graph Laplacian regularization to capture the underlying social relationship among the users. Differently from the previous ap...

A Rank Correlation Model Class for Exceptional Model Mining

the use of appropriate madm model for ranking the vendors of mci equipments using fuzzy approach

abstract nowadays, the science of decision making has been paid to more attention due to the complexity of the problems of suppliers selection. as known, one of the efficient tools in economic and human resources development is the extension of communication networks in developing countries. so, the proper selection of suppliers of tc equipments is of concern very much. in this study, a ...

Location and Activity Recommendation by Using Consecutive Itinerary Matching Model

In fact, most people have had the experience that they haven’t made detailed itinerary in advance before a journey, and as a result they don’t know what place or what kind of activity is suitable as the next visit location and activity after they engage in an activity in a certain place. To alleviate such problem, in this paper, we proposed the Consecutive Itinerary Matching Model to help mobil...

Kernelized rank learning for personalized drug recommendation.

Motivation Large-scale screenings of cancer cell lines with detailed molecular profiles against libraries of pharmacological compounds are currently being performed in order to gain a better understanding of the genetic component of drug response and to enhance our ability to recommend therapies given a patient's molecular profile. These comprehensive screens differ from the clinical setting in...