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FOCoR: A Course Recommendation Approach Based on Feature Selection Optimization

WANG Yang;CHEN Mei;LI Hui;College of Computer Science and Technology,Guizhou University;  
To solve the cold start problem of the recommendation model based on the behavioral log from online education platform,we design a course recommendation method named FOCoR that integrates data of course selection. First,we propose a technology of feature selection based on genetic algorithm( FSBGA),and then take the result of feature selection as input to build a recommendation model based on Light GBM which is a technology of gradient boosting tree for course recommendation. To be more specific,we construct a fitness function combining the loss of model and the number of features in the proposed FSBGA so that we successfully searched out the optimal feature subset that takes into account the loss of model and the number of features in the feature subset space of university course selection data. According to three indicators of log loss,F1-score and AUC,the model of course selection trained on the feature subset selected by the FSBGA is better than the models trained on the others selected by algorithms based on mutual information or F-test. In order to verify the effectiveness of the work in this paper,we have tested and evaluated FOCoR,Light GBM,XGBoost,decision tree,random forest,logistic regression and other algorithms on real data sets,and the results show that FOCoR has achieved the best performance in F1 scores.
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