feat: implement NGCF model #20

models/ngcf.py

@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+
+from models.base_model import BaseModel
+
+class NGCF(BaseModel):
+
+    def __init__(self, cfg, num_users, num_items, laplacian_matrix):
+        super().__init__()
+        self.num_users = num_users
+        self.laplacian_matrix = laplacian_matrix
+        self.embedding = nn.Embedding(
+            num_users+num_items, cfg.embed_size, dtype=torch.float32)
+
+        self.W1 = nn.ModuleList([
+            nn.Linear(cfg.embed_size, cfg.embed_size, bias=False) for _ in range(cfg.num_orders) 
+            ])
+        self.W2 = nn.ModuleList([
+            nn.Linear(cfg.embed_size, cfg.embed_size, bias=False) for _ in range(cfg.num_orders)
+            ])
+
+    def _init_weights(self):
+        for child in self.children():
+            if isinstance(child, nn.Embedding):
+                nn.init.xavier_uniform_(child.weight)k
+
+    def forward(self, user_id, item_id):
+        user_embed_list, item_embed_list = [self.embedding(user_id),], [self.embedding(self.num_users+item_id)]
+        last_embed = self.embedding
+        for w1, w2 in zip(self.W1, self.W2):
+            last_embed = embedding_propagation(last_embed, w1, w2)
+            user_embed_list.append(last_embed(user_id))
+            item_embed_list.append(last_embed(self.num_users+item_id))
+
+        user_embed = torch.concat(user_embed_list, dim=1)
+        item_embed = torch.concat(item_embed_list, dim=1)
+
+        return torch.sum(user_emb * item_emb, dim=1)
+
+    def embedding_propagation(self, last_embed, w1, w2):
+        identity_matrix = torch.eye(*self.laplacian_matrix.size())
+        term1 = torch.matmul(self.laplacian_matrix + identity_matrix, last_embed)
+        term1 = w1(term1)
+
+        neighbor_embeddings = torch.matmul(self.laplacian_matrix, last_embed)
+        term2 = torch.mul(neighbor_embeddings, last_embed)
+        term2 = w2(term2)
+
+        return nn.functional.leaky_relu(term1 + term2)
+