推荐系统实践(基于近邻和LFM的推荐系统)
这是《推荐系统实践》中基于近邻的方法的代码实现。
数据集使用的是MovieLen中大小为100K的数据集。
程序分为6个文件：
UserCF:基于用户的算法
UserCF_IIF：改进的基于用户的算法
ItemCF：基于物品的算法
ItemCF_IUF：改进的基于物品的算法
LFM:隐因子模型算法
Evaluation：评价指标
mainCF:主函数，读取数据和测试
precision = 0.048507
recall = 0.915210
coverage = 0.995761
popularity = 3.984490
结果如上，准确率这么低，还有，你在构造数据的时候，有评分的样本定义为1，没有构造负样本吗？这个过程在哪里？求指点！
I ran your mainCF, and found the precision is very low. I think you need change sorted(rank.items(), key = operator.itemgetter(1), reverse = True) to R = sorted(rank.items(), key = operator.itemgetter(1), reverse = True)[0:K] in UserCF.Recommendation()
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