diff --git a/rhj/backend/app/models/recommend/LightGCN.py b/rhj/backend/app/models/recommend/LightGCN.py
new file mode 100644
index 0000000..38b1732
--- /dev/null
+++ b/rhj/backend/app/models/recommend/LightGCN.py
@@ -0,0 +1,121 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import scipy.sparse as sp
+import math
+import networkx as nx
+import random
+from copy import deepcopy
+from app.utils.parse_args import args
+from app.models.recommend.base_model import BaseModel
+from app.models.recommend.operators import EdgelistDrop
+from app.models.recommend.operators import scatter_add, scatter_sum
+
+
+init = nn.init.xavier_uniform_
+
+class LightGCN(BaseModel):
+    def __init__(self, dataset, pretrained_model=None, phase='pretrain'):
+        super().__init__(dataset)
+        self.adj = self._make_binorm_adj(dataset.graph)
+        self.edges = self.adj._indices().t()
+        self.edge_norm = self.adj._values()
+
+        self.phase = phase
+
+        self.emb_gate = lambda x: x
+
+        if self.phase == 'pretrain' or self.phase == 'vanilla' or self.phase == 'for_tune':
+            self.user_embedding = nn.Parameter(init(torch.empty(self.num_users, self.emb_size)))
+            self.item_embedding = nn.Parameter(init(torch.empty(self.num_items, self.emb_size)))
+
+
+        elif self.phase == 'finetune':
+            pre_user_emb, pre_item_emb = pretrained_model.generate()
+            self.user_embedding = nn.Parameter(pre_user_emb).requires_grad_(True)
+            self.item_embedding = nn.Parameter(pre_item_emb).requires_grad_(True)
+
+        elif self.phase == 'continue_tune':
+            # re-initialize for loading state dict
+            self.user_embedding = nn.Parameter(init(torch.empty(self.num_users, self.emb_size)))
+            self.item_embedding = nn.Parameter(init(torch.empty(self.num_items, self.emb_size)))
+
+        self.edge_dropout = EdgelistDrop()
+
+    def _agg(self, all_emb, edges, edge_norm):
+        src_emb = all_emb[edges[:, 0]]
+
+        # bi-norm
+        src_emb = src_emb * edge_norm.unsqueeze(1)
+
+        # conv
+        dst_emb = scatter_sum(src_emb, edges[:, 1], dim=0, dim_size=self.num_users+self.num_items)
+        return dst_emb
+    
+    def _edge_binorm(self, edges):
+        user_degs = scatter_add(torch.ones_like(edges[:, 0]), edges[:, 0], dim=0, dim_size=self.num_users)
+        user_degs = user_degs[edges[:, 0]]
+        item_degs = scatter_add(torch.ones_like(edges[:, 1]), edges[:, 1], dim=0, dim_size=self.num_items)
+        item_degs = item_degs[edges[:, 1]]
+        norm = torch.pow(user_degs, -0.5) * torch.pow(item_degs, -0.5)
+        return norm
+
+    def forward(self, edges, edge_norm, return_layers=False):
+        all_emb = torch.cat([self.user_embedding, self.item_embedding], dim=0)
+        all_emb = self.emb_gate(all_emb)
+        res_emb = [all_emb]
+        for l in range(args.num_layers):
+            all_emb = self._agg(res_emb[-1], edges, edge_norm)
+            res_emb.append(all_emb)
+        if not return_layers:
+            res_emb = sum(res_emb)
+            user_res_emb, item_res_emb = res_emb.split([self.num_users, self.num_items], dim=0)
+        else:
+            user_res_emb, item_res_emb = [], []
+            for emb in res_emb:
+                u_emb, i_emb = emb.split([self.num_users, self.num_items], dim=0)
+                user_res_emb.append(u_emb)
+                item_res_emb.append(i_emb)
+        return user_res_emb, item_res_emb
+    
+    def cal_loss(self, batch_data):
+        edges, dropout_mask = self.edge_dropout(self.edges, 1-args.edge_dropout, return_mask=True)
+        edge_norm = self.edge_norm[dropout_mask]
+
+        # forward
+        users, pos_items, neg_items = batch_data
+        user_emb, item_emb = self.forward(edges, edge_norm)
+        batch_user_emb = user_emb[users]
+        pos_item_emb = item_emb[pos_items]
+        neg_item_emb = item_emb[neg_items]
+        rec_loss = self._bpr_loss(batch_user_emb, pos_item_emb, neg_item_emb)
+        reg_loss = args.weight_decay * self._reg_loss(users, pos_items, neg_items)
+
+        loss = rec_loss + reg_loss
+        loss_dict = {
+            "rec_loss": rec_loss.item(),
+            "reg_loss": reg_loss.item(),
+        }
+        return loss, loss_dict
+    
+    @torch.no_grad()
+    def generate(self, return_layers=False):
+        return self.forward(self.edges, self.edge_norm, return_layers=return_layers)
+    
+    @torch.no_grad()
+    def generate_lgn(self, return_layers=False):
+        return self.forward(self.edges, self.edge_norm, return_layers=return_layers)
+    
+    @torch.no_grad()
+    def rating(self, user_emb, item_emb):
+        return torch.matmul(user_emb, item_emb.t())
+    
+    def _reg_loss(self, users, pos_items, neg_items):
+        u_emb = self.user_embedding[users]
+        pos_i_emb = self.item_embedding[pos_items]
+        neg_i_emb = self.item_embedding[neg_items]
+        reg_loss = (1/2)*(u_emb.norm(2).pow(2) +
+                          pos_i_emb.norm(2).pow(2) +
+                          neg_i_emb.norm(2).pow(2))/float(len(users))
+        return reg_loss