wehub-resource-sync
96 lines
3.6 KiB
Python
96 lines
3.6 KiB
Python
from typing import Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from easygraph.classes import Hypergraph
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class DHCF(nn.Module):
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r"""The DHCF model proposed in `Dual Channel Hypergraph Collaborative Filtering <https://dl.acm.org/doi/10.1145/3394486.3403253>`_ paper (KDD 2020).
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.. note::
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The user and item embeddings and trainable parameters are initialized with xavier_uniform distribution.
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Parameters:
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``num_users`` (``int``): The Number of users.
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``num_items`` (``int``): The Number of items.
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``emb_dim`` (``int``): Embedding dimension.
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``num_layers`` (``int``): The Number of layers. Defaults to ``3``.
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``drop_rate`` (``float``): The dropout probability. Defaults to ``0.5``.
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"""
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def __init__(
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self,
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num_users: int,
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num_items: int,
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emb_dim: int,
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num_layers: int = 3,
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drop_rate: float = 0.5,
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) -> None:
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super().__init__()
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self.num_users, self.num_items = num_users, num_items
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self.num_layers = num_layers
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self.drop_rate = drop_rate
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self.u_embedding = nn.Embedding(num_users, emb_dim)
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self.i_embedding = nn.Embedding(num_items, emb_dim)
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self.W_gc, self.W_bi = nn.ModuleList(), nn.ModuleList()
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for _ in range(self.num_layers):
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self.W_gc.append(nn.Linear(emb_dim, emb_dim))
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self.W_bi.append(nn.Linear(emb_dim, emb_dim))
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self.reset_parameters()
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def reset_parameters(self):
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r"""Initialize learnable parameters."""
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nn.init.xavier_uniform_(self.u_embedding.weight)
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nn.init.xavier_uniform_(self.i_embedding.weight)
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for W_gc, W_bi in zip(self.W_gc, self.W_bi):
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nn.init.xavier_uniform_(W_gc.weight)
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nn.init.xavier_uniform_(W_bi.weight)
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nn.init.constant_(W_gc.bias, 0)
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nn.init.constant_(W_bi.bias, 0)
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def forward(
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self, hg_ui: Hypergraph, hg_iu: Hypergraph
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) -> Tuple[torch.Tensor, torch.Tensor]:
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r"""The forward function.
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Parameters:
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``hg_ui`` (``eg.Hypergraph``): The hypergraph structure that users as vertices.
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``hg_iu`` (``eg.Hypergraph``): The hypergraph structure that items as vertices.
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"""
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u_embs = self.u_embedding.weight
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i_embs = self.i_embedding.weight
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all_embs = torch.cat([u_embs, i_embs], dim=0)
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embs_list = [all_embs]
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for _idx in range(self.num_layers):
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u_embs, i_embs = torch.split(
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all_embs, [self.num_users, self.num_items], dim=0
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)
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# ==========================================================================================
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# Two JHConv Layers for users and items, respectively.
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u_embs = hg_ui.smoothing_with_HGNN(u_embs)
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i_embs = hg_iu.smoothing_with_HGNN(i_embs)
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g_embs = torch.cat([u_embs, i_embs], dim=0)
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sum_embs = F.leaky_relu(
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self.W_gc[_idx](g_embs) + g_embs, negative_slope=0.2
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)
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# ==========================================================================================
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bi_embs = all_embs * g_embs
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bi_embs = F.leaky_relu(self.W_bi[_idx](bi_embs), negative_slope=0.2)
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all_embs = sum_embs + bi_embs
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all_embs = F.dropout(all_embs, p=self.drop_rate, training=self.training)
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all_embs = F.normalize(all_embs, p=2, dim=1)
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embs_list.append(all_embs)
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embs = torch.stack(embs_list, dim=1)
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embs = torch.mean(embs, dim=1)
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u_embs, i_embs = torch.split(embs, [self.num_users, self.num_items], dim=0)
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return u_embs, i_embs
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