On the combination of user-based and item-based collaborative filtering
On the combination of user-based and item-based collaborative filtering
复制标题
基于用户和基于项目的协同过滤的结合
DOI：
10.1080/03057920412331272199
复制
发表时间：
2004
影响因子：
1.8
通讯作者：
K. Margaritis
中科院分区：
文献类型：
--
作者：
M. Vozalis;K. Margaritis
在本文中，我们提出了两个新的过滤算法，这是一个基于用户和基于项目的协同过滤方案的组合。第一个，Hybrid-Ib，确定了一个合理的大邻域相似的用户，然后使用这个子集，以获得基于项目的推荐模型。第二种算法Hybrid-CF首先定位与我们想要预测的项目相似的项目，然后基于该邻域生成基于用户的预测。我们首先描述的算法的执行步骤，并进行扩展实验。我们的结论是，我们的算法是直接可比现有的过滤方法，与混合CF产生有利的，或在最坏的情况下，在所有选定的评估指标类似的结果。电子邮件：kmarg@uom.gr
In this paper, we propose two new filtering algorithms which are a combination of user-based and item-based collaborative filtering schemes. The first one, Hybrid-Ib, identifies a reasonably large neighbourhood of similar users and then uses this subset to derive the item-based recommendation model. The second algorithm, Hybrid-CF, starts by locating items similar to the one for which we want a prediction, and then, based on that neighbourhood, it generates its user-based predictions. We start by describing the execution steps of the algorithms and proceed with extended experiments. We conclude that our algorithms are directly comparable to existing filtering approaches, with Hybrid-CF producing favorable or, in the worst case, similar results in all selected evaluation metrics. E-mail: kmarg@uom.gr