A Local Scoring Model for Diversified Recommendation

Collaborative filtering recommender systems suffer from lack of diversity as well as the scalability and the sparsity problems. Introduced is a new recommendation idea called LCSC that generates recommendations from those who previously recommended the target customers successfully while CF generates recommendations from those who are most similar to the target customer. The LCSC method is characterized by (1) the establishment and use of recommender networks to maintain recommendation history that serves as the basis of recommendation decision and (2) a small scope of search for supportive neighbors from whom recommended items are collected. Experiments with real transactional data confirm that LCSC\u27s recommendation accuracy is as good as CF\u27s, while LCSC has significant advantages over CF in computational efficiency and recommendation diversity