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View Code? Open in Web Editor NEW[SIGIR'22] Official PyTorch implementation for "Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering".
[SIGIR'22] Official PyTorch implementation for "Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering".
Very good job! I think "dataset: ml-1m“ should be added in the file "model-rgcf.yaml"
大佬您好!您论文里边写的β分布在[0.02,1]里边,但是代码里调试的时候几个数据集生成的sim_value的分布都不太均匀,<0.2一个值都没有,请问是要加大这个值进行去噪还是先归一化再调整β呢
这个电影的结果跑好像跟论文里不一样
best valid score:
recall@10 :0.166 recall@20 :0.2524 recall@50 :0.4067
mrr@10 :0.3651 mrr@20 :0.3728 mrr@50 :0.3757
ndcg@10 :0.2024 ndcg@20 :0.2201 ndcg@50 :0.2687
hit@10 :0.7132 hit@20 :0.8203 hit@50 :0.9082
12 Nov 18:00 INFO
test result:
recall@10 :0.1885 recall@20 :0.2784 recall@50 :0.4326
mrr@10 :0.4347 mrr@20 :0.4415 mrr@50 :0.4442
ndcg@10 :0.2492 ndcg@20 :0.2603 ndcg@50 :0.304
hit@10 :0.7388 hit@20 :0.8344 hit@50 :0.9142
你好 ,还有个问题就是,使用GNN聚合一节邻居信息去噪,再将学习到的嵌入送到GNN中,这样每个节点的嵌入不已经有噪声了么,这个该怎么解释呢?谢谢
作者您好,请问您代码中的coverage部分是怎么计算的?在 if item_feat is not None:
rec_items = struct.get('rec.items')
user_num = rec_items.shape[0]
diversity_res = 'diversity_res: \n'这部分,item_feat接收什么参数计算?
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