Comments (1)
Thank you for your interest.
About the relationship between Equation 1 and Equation 5, Equation 1 means the aggregating process for constructing the item-item semantic graph ( for generating the item embeddings); but Equation 5 is the aggregation in collaborative graph (\ie user-item graph).
As for the kNN sparsification, it makes differentiation by calculating the similarity between item embeddings aggregated from Equation 1. Then for the purpose of top-k function, it utilizes the aggregated embeddings in Equation 1 for graph contrusting, the selection of top-k highest scoring edges aims to decrease the computational demanding. This wouldn't bring in your mentioned "gradient break" problem, for that the top-k selected ones would be propagated . (You could refer to this paper for kNN sparsification: Mining Latent Structures for Multimedia Recommendation 2021 MM, or other related kNN papers.)
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Related Issues (19)
- 代码还没更新吗?
- 关于超参数的一些疑问 HOT 1
- About the Movielens-1M dataset HOT 1
- 模型实现与论文描述 HOT 2
- Book数据集运行配置? HOT 9
- 关于环境配置问题 HOT 2
- There is a discrepancy between the model code and the paper? HOT 1
- Requirements request HOT 1
- 关于代码跑recall指标 HOT 3
- 数据集读入问题 HOT 2
- 超参数设置问题 HOT 1
- 数据集问题 HOT 1
- 代码应用 HOT 1
- 數據集問題
- 指标和论文不一致问题
- 可视化 HOT 1
- 训练时间长 HOT 1
- 想请问一下为什么论文里写的是BprLoss而代码写的BceLoss HOT 1
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