Collaborative filtering technologies are facing two major challenges: scalability and recommendation quality, which are two goals in conflict. Nowadays more studies are focusing on the quality issue but less on the scalability one. We introduce a genetic clustering algorithm to partition the source data, guaranteeing that the intra-similarity will be high but the inter-similarity will be low. The clustering process is off-line running. Our empirical results show that the genetic clustering based collaborative filtering recommender system outperforms the memory-based one in scalability, and outperforms the k-means clustering based one and the memory-based one in recommendation quality.
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