In this paper, we present a collaborative recommend system that recommends elective courses for students based on similarities of student’s grades obtained in the last semester. The proposed system employs data mining techniques to discover patterns between grades. Consequently, we have noticed that clustering students into similar groups by performing clustering. The data set is processed for clustering in such a way that it produces optimal number of clusters.

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A Hybrid Approach to Service Recommendation Based on Network Representation Learning

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A Hybrid Approach to Service Recommendation Based on Network Representation Learning

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This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014. There are 4 columns in the file, which represent viewer ID, the current channel number, the next channel number, the date of the month, respectively. The first column, the ID code of a viewer, ranks in descent with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

 

 

 

 

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This dataset includes  the Channels Switch Sequences of 300 IPTV viewers in Guangzhou, P.R. China, in Augest, 2014.

There are 4 columns in the file, which represent viewer ID, the current channel number, th next channel number, the date of the month, respectively.

The first column, the ID code of a viewter, ranks with the times the viewer watched tv channels. The more times a viewer watches tv channels, the bigger

the ID is. In a day, the rows are time series and generated step by step as the real watching tv behavior. 

 

 

 

 

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71 Views