Abstract: A model-based recommendation algorithm was proposed, which uses Multi-level Association Rules (MAR) to alleviate those problems about data sparseness and scalability of the recent recommendation algorithm. In this algorithm, a model for preference prediction was built by using multi-level association rule mining, which is used to compute preferences for items. The experimental results show that performance of the algorithm is superior to other methods.

Key words: multi-level association rules, collaborative filtering, recommendation algorithm, e-commerce

摘要： 提出一种基于多层关联规则(MAR)的推荐算法，着重解决目前推荐算法的稀疏性问题和可扩展性问题。该算法采用多层关联规则挖掘用户对商品的偏好，并建立用户偏好预测模型。实验表明该算法性能优于其他推荐算法。

关键词: 多层关联规则, 协同过滤, 推荐算法, 电子商务