Using Genetic Algorithms for Personalized Recommendation 111 GAS+CH 02 0.I 15 Fig 3 Comparison of recall between the proposed approach and the traditional CF algorithm →GAs+CF -TradtonaICP Fig. 4. Comparison of FI between the proposed approach and the traditional CF algorithm onclusions In this paper, we have proposed a hybrid recommender system based on GAs and collaborative filtering technique. The system integrates data from various sources (product, customer, and transaction data)to form the customer preference profile. The GAs are applied to optimize a vector of the feature weights, which are used to meas- ure the similarity among customers. Incorporating weighting information into the collaborative filtering process has proven to be more effective than traditional one References 1. Schafer, J B, Konstan, J, Riedl, J. Electronic commerce recommender applications. Jour nal of Data Mining and Knowledge disc overy 2. Karypis, G. Evaluation of item-based top-n recommendation algorithms. In: Proceedings of the 10th International Conference on Information and Knowledge Management, pr 247-254(2001)Using Genetic Algorithms for Personalized Recommendation 111 Fig. 3. Comparison of recall between the proposed approach and the traditional CF algorithm Fig. 4. Comparison of F1 between the proposed approach and the traditional CF algorithm 5 Conclusions In this paper, we have proposed a hybrid recommender system based on GAs and collaborative filtering technique. The system integrates data from various sources (product, customer, and transaction data) to form the customer preference profile. The GAs are applied to optimize a vector of the feature weights, which are used to meas￾ure the similarity among customers. Incorporating weighting information into the collaborative filtering process has proven to be more effective than traditional one. References 1. Schafer, J.B., Konstan, J., Riedl, J.: Electronic commerce recommender applications. Jour￾nal of Data Mining and Knowledge Discovery 5(1/2), 115–152 (2000) 2. Karypis, G.: Evaluation of item-based top-n recommendation algorithms. In: Proceedings of the 10th International Conference on Information and Knowledge Management, pp. 247–254 (2001)