The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Explaining collaborative filtering recommendations
CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
E-Commerce Recommendation Applications
Data Mining and Knowledge Discovery
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Personalized expert-based recommender system: training C-SVM for personalized expert identification
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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Due to the explosion of e-commerce, recommender systems are rapidly becoming a core tool to accelerate cross-selling and strengthen customer loyalty. There are two prevalent approaches for building recommender systems – content-based recommending and collaborative filtering (CF). This study focuses on improving the performance of recommender systems by using data mining techniques. This paper proposes an SVM based recommender system. Furthermore this paper presents the methods for improving the performance of the SVM based recommender system in two aspects: feature subset selection and parameter optimization. GA is used to optimize both the feature subset and parameters of SVM simultaneously for the recommendation problem. The results of the evaluation experiment show the proposed model's improvement in making recommendations.