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[SIGIR'2023] "MAERec: Graph Masked Autoencoder for Sequential Recommendation"
Home Page: https://arxiv.org/abs/2305.04619
您好,文中的数据集中neg的99个负样本中包含大量的1,请问这样的负样本采样方法是有什么依据吗,这样做是否会影响实验的公平性
Hello, thanks for your outstanding work!
I am interested in your paper "Adaptive Path Masking", the idea of expanding and sampling the nodes in the graph.
Is it a kind of Heuristically Search?
Hi,
It's not easy for me to connect the implementation of local graph to the sampling of candidates.
It would be helpful if you could provide some detailed references.
Thanks !
你好,直接通过python main.py --data books
是无法run的。应该是缺少对数据集的处理步骤。
您好!请问一下两个数据集的预处理操作是什么?选取了哪一段时间的数据才能处理成文中提到的密度呢?
除了论文中使用的三个数据集,是否有办法处理其他数据集,使其也能在该模型上运行。比如ML-1M、Yelp和其他数据集。是否能提供数据集预处理的源代码呢?
另外,我对数据集的预处理操作存在些许疑问,除了对数据集进行了5-core操作,还进行了其他什么处理吗?我利用其他开源框架,比如伯乐(DuoRec的实现),始终无法重现论文中提供的数据集情况。
你好,论文中并没有看到模型在做预测时的操作,请问是如往常论文一样取一个正例,随机取100个负例,然后在这101个实例中计算指标吗
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