Comments (4)
huangtinglin
commented on September 7, 2024
图推荐是讲原本的推荐问题，即判断一个用户与某个商品的相关性，看成是一个边预测问题。实际运用即现在某个user-item-KG上训练，然后预测user-item边存在的概率，与我们文中的实验与实验数据类似。
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cabbgae
commented on September 7, 2024
非常感谢您的回答！我想接着这个问题再问一下，能否用公式14，
来确定user对每个item的评分，从而达到推荐的目的。
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cabbgae
commented on September 7, 2024
还有一点，在代码中，公式14体现为
能具体说说此时pos_score和neg_score分别代表什么吗，有什么具体含义。非常感谢您能在百忙之中抽出时间来回答！
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huangtinglin
commented on September 7, 2024
代表了user和正样本或者负样本的相似性
…Sent from my iPhone
On May 21, 2022, at 14:10, cabbage ***@***.***> wrote:
还有一点，在代码中，公式14体现为
[image]<https://user-images.githubusercontent.com/88702772/169638389-0d2fec22-00dd-441e-9d15-d364cdda0b9b.png>
能具体说说此时pos_score和neg_score分别代表什么吗，有什么具体含义。非常感谢您能在百忙之中抽出时间来回答！
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