New conditional probability-based collaborative filtering for the binary market basket data with the high dimensional cold-start problem

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Recommendation systems are crucial for customer relationship management in marketing. There exist a lot of collaborative filtering techniques for recommender systems. The Pearson correlation-based approaches have been utilized for collaborative filtering using the binary market basket data with the high-dimensional cold-start problem. As an alternative of the Pearson correlation-based approaches, we propose a new conditional probability-based collaborative filtering based on Bayes theorem for the binary market basket data with the high-dimensional cold-start problem. The proposed conditional probability-based collaborative filtering outperforms the existing approaches based on the experimental results.