Master tutors are important guides in the academic career of postgraduate students, so find and choose a suitable tutor is very important. However, the existing master tutor selection mode has many problems such as information asymmetry, which makes it difficult for students to make the most appropriate choice. With the construction of smart campus, more and more educational data are recorded, which makes it possible to conduct Educational Data Mining. In this paper, master tutors and students were respectively modeled, a master tutor recommendation method based on machine learning algorithms, such as TF-IDF, kNN and SVDCF was introduced. A real data set of our university was built and preprocessed. Specific recommendation algorithms were then designed. Experiments were conducted and acceptable Top-N hit rate results were achieved. The experimental results show that based on the modeling of students and master tutors, a lightweight combination of machine learning algorithms can achieve good practical results.

In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. We also show how to use knowledge about heterogeneity to aid in feature selection.