一种基于多样性的top-N推荐算法

A top-N recommendation algorithm based on diversity

目前大多数推荐算法都是以提高用户对未知商品的评分预测值为主要目标。然而预测准确率并不是增加用户满意度的唯一标准,推荐列表的多样性也是衡量推荐质量的一个重要指标。提出了一种新的推荐方法,在保证推荐列表准确率的条件下,通过调节商品类的权重来提高推荐商品的多样性。实验表明,该方法不仅具有较低的时间复杂度和高度的可扩展性,而且与其他方法相比能够获得更好的推荐效果。

The major objecive of recommendation algorithms is to accurately predict users＇rating val- ues of the unknown product items. However, it has recognized that an accurate prediction of rating values is not the only way for achieving user satisfaction, and the diversity of recommendation lists has gained importance recently. In this study, we propose a novel method which can adjust the weights of product categories to enhance the diversity levels of their own recommendation lists with little decrease in accura- cy. Experimental results show that our method outperforms other methods,which has a very low compu- tational time complexity and is highly scalable.