新增管理员页面和用户申诉、迁移审核页面，推荐系统
Change-Id: Ief5646321feb98fadb17da4b4e91caeaacdbacc5
diff --git a/.gitignore b/.gitignore
index 1928d6a..dcb56f0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,3 +2,5 @@
.vscode/
torrents/
front/node_moduels
+recommend/model/__pycache__
+recommend/utils/__pycache__
\ No newline at end of file
diff --git a/front/src/AdminPage.js b/front/src/AdminPage.js
new file mode 100644
index 0000000..4862449
--- /dev/null
+++ b/front/src/AdminPage.js
@@ -0,0 +1,145 @@
+import React, { useState } from "react";
+import { useNavigate } from "react-router-dom";
+
+// 示例数据
+const initialConfig = {
+ FarmNumber: 3,
+ FakeTime: 3,
+ BegVote: 3,
+ CheatTime: 5,
+};
+
+const cheatUsers = [
+ { user_id: "u001", email: "cheat1@example.com", username: "cheater1", account_status: 1 },
+ { user_id: "u002", email: "cheat2@example.com", username: "cheater2", account_status: 0 },
+];
+
+const suspiciousUsers = [
+ { user_id: "u101", email: "suspect1@example.com", username: "suspect1", account_status: 0 },
+ { user_id: "u102", email: "suspect2@example.com", username: "suspect2", account_status: 0 },
+];
+
+export default function AdminPage() {
+ const navigate = useNavigate();
+ const [config, setConfig] = useState(initialConfig);
+
+ const handleConfigChange = (e) => {
+ const { name, value } = e.target;
+ setConfig({ ...config, [name]: value });
+ };
+
+ const handleBan = (user) => {
+ alert(`已封禁用户：${user.username}`);
+ };
+
+ return (
+ <div style={{ padding: 40, maxWidth: 900, margin: "0 auto" }}>
+ <h1 style={{ textAlign: "center", marginBottom: 32 }}>管理员页面</h1>
+ {/* 参数设置 */}
+ <div style={{ marginBottom: 32, padding: 18, background: "#f7faff", borderRadius: 12, display: "flex", gap: 24, alignItems: "center" }}>
+ <b>系统参数：</b>
+ <label>
+ FarmNumber:
+ <input type="number" name="FarmNumber" value={config.FarmNumber} onChange={handleConfigChange} style={{ width: 60, margin: "0 12px" }} />
+ </label>
+ <label>
+ FakeTime:
+ <input type="number" name="FakeTime" value={config.FakeTime} onChange={handleConfigChange} style={{ width: 60, margin: "0 12px" }} />
+ </label>
+ <label>
+ BegVote:
+ <input type="number" name="BegVote" value={config.BegVote} onChange={handleConfigChange} style={{ width: 60, margin: "0 12px" }} />
+ </label>
+ <label>
+ CheatTime:
+ <input type="number" name="CheatTime" value={config.CheatTime} onChange={handleConfigChange} style={{ width: 60, margin: "0 12px" }} />
+ </label>
+ </div>
+ {/* 作弊用户 */}
+ <div style={{ marginBottom: 32 }}>
+ <h2 style={{ color: "#e53935" }}>作弊用户</h2>
+ <table style={{ width: "100%", background: "#fff", borderRadius: 10, boxShadow: "0 2px 8px #e0e7ff", marginBottom: 18 }}>
+ <thead>
+ <tr style={{ background: "#f5f5f5" }}>
+ <th>user_id</th>
+ <th>email</th>
+ <th>username</th>
+ <th>account_status</th>
+ <th>操作</th>
+ </tr>
+ </thead>
+ <tbody>
+ {cheatUsers.map((u) => (
+ <tr key={u.user_id}>
+ <td>{u.user_id}</td>
+ <td>{u.email}</td>
+ <td>{u.username}</td>
+ <td style={{ color: u.account_status === 1 ? "#e53935" : "#43a047" }}>
+ {u.account_status === 1 ? "封禁" : "正常"}
+ </td>
+ <td>
+ <button
+ style={{ background: "#e53935", color: "#fff", border: "none", borderRadius: 6, padding: "4px 14px", cursor: "pointer" }}
+ onClick={() => handleBan(u)}
+ >
+ 封禁
+ </button>
+ </td>
+ </tr>
+ ))}
+ </tbody>
+ </table>
+ </div>
+ {/* 可疑用户 */}
+ <div style={{ marginBottom: 32 }}>
+ <h2 style={{ color: "#ff9800" }}>可疑用户</h2>
+ <table style={{ width: "100%", background: "#fff", borderRadius: 10, boxShadow: "0 2px 8px #e0e7ff" }}>
+ <thead>
+ <tr style={{ background: "#f5f5f5" }}>
+ <th>user_id</th>
+ <th>email</th>
+ <th>username</th>
+ <th>account_status</th>
+ <th>操作</th>
+ </tr>
+ </thead>
+ <tbody>
+ {suspiciousUsers.map((u) => (
+ <tr key={u.user_id}>
+ <td>{u.user_id}</td>
+ <td>{u.email}</td>
+ <td>{u.username}</td>
+ <td style={{ color: u.account_status === 1 ? "#e53935" : "#43a047" }}>
+ {u.account_status === 1 ? "封禁" : "正常"}
+ </td>
+ <td>
+ <button
+ style={{ background: "#e53935", color: "#fff", border: "none", borderRadius: 6, padding: "4px 14px", cursor: "pointer" }}
+ onClick={() => handleBan(u)}
+ >
+ 封禁
+ </button>
+ </td>
+ </tr>
+ ))}
+ </tbody>
+ </table>
+ </div>
+ {/* 跳转按钮 */}
+ <div style={{ display: "flex", gap: 24, justifyContent: "center" }}>
+ <button
+ style={{ background: "#1976d2", color: "#fff", border: "none", borderRadius: 8, padding: "10px 28px", fontWeight: 600, fontSize: 16, cursor: "pointer" }}
+ onClick={() => navigate("/appeal-review")}
+ >
+ 用户申诉
+ </button>
+ <button
+ style={{ background: "#43a047", color: "#fff", border: "none", borderRadius: 8, padding: "10px 28px", fontWeight: 600, fontSize: 16, cursor: "pointer" }}
+ onClick={() => navigate("/migration-review")}
+ >
+ 用户迁移
+ </button>
+ </div>
+ </div>
+ );
+}
\ No newline at end of file
diff --git a/front/src/App.js b/front/src/App.js
index 2f85943..372fa62 100644
--- a/front/src/App.js
+++ b/front/src/App.js
@@ -25,6 +25,10 @@
import LoginPage from './LoginPage';
import RegisterPage from './RegisterPage';
import RequireAuth from './RequireAuth';
+import AdminPage from './AdminPage';
+import AppealPage from './AppealPage';
+import MigrationPage from './MigrationPage';
+
const navItems = [
{ label: "电影", icon: <MovieIcon />, path: "/movie" },
@@ -164,6 +168,9 @@
<Route path="/user" element={<UserProfile />} />
<Route path="/publish" element={<PublishPage />} />
<Route path="/torrent/:torrentId" element={<TorrentDetailPage />} />
+ <Route path="/admin" element={<AdminPage />} />
+ <Route path="/appeal-review" element={<AppealPage />} />
+ <Route path="/migration-review" element={<MigrationPage />} />
</Route>
</Routes>
</Router>
diff --git a/front/src/AppealPage.js b/front/src/AppealPage.js
new file mode 100644
index 0000000..a0314c4
--- /dev/null
+++ b/front/src/AppealPage.js
@@ -0,0 +1,141 @@
+import React, { useState } from "react";
+
+// 示例申诉数据
+const appeals = [
+ {
+ appeal_id: "a001",
+ user_id: "u001",
+ content: "我没有作弊，请审核我的账号。",
+ file_url: "http://sse.bjtu.edu.cn/media/attachments/2024/10/20241012160658.pdf",
+ status: 0,
+ },
+ {
+ appeal_id: "a002",
+ user_id: "u002",
+ content: "误封申诉，详见附件。",
+ file_url: "http://sse.bjtu.edu.cn/media/attachments/2024/10/20241012160658.pdf",
+ status: 1,
+ },
+];
+
+// 简单PDF预览组件
+function FileViewer({ url }) {
+ if (!url) return <div>无附件</div>;
+ if (url.endsWith(".pdf")) {
+ return (
+ <iframe
+ src={url}
+ title="PDF预览"
+ width="100%"
+ height="400px"
+ style={{ border: "1px solid #ccc", borderRadius: 8 }}
+ />
+ );
+ }
+ // 这里只做PDF示例，实际可扩展为DOC等
+ return <a href={url} target="_blank" rel="noopener noreferrer">下载附件</a>;
+}
+
+export default function AppealPage() {
+ const [selectedId, setSelectedId] = useState(appeals[0].appeal_id);
+ const selectedAppeal = appeals.find(a => a.appeal_id === selectedId);
+
+ const handleApprove = () => {
+ alert("已通过申诉（示例，无实际状态变更）");
+ };
+ const handleReject = () => {
+ alert("已拒绝申诉（示例，无实际状态变更）");
+ };
+
+ return (
+ <div style={{ display: "flex", minHeight: "100vh", background: "#f7faff" }}>
+ {/* 侧栏 */}
+ <div style={{ width: 180, background: "#fff", borderRight: "1px solid #e0e7ff", padding: 0 }}>
+ <h3 style={{ textAlign: "center", padding: "18px 0 0 0", color: "#1976d2" }}>申诉列表</h3>
+ <div style={{ display: "flex", flexDirection: "column", gap: 12, marginTop: 18 }}>
+ {appeals.map(a => (
+ <div
+ key={a.appeal_id}
+ onClick={() => setSelectedId(a.appeal_id)}
+ style={{
+ margin: "0 12px",
+ padding: "16px 10px",
+ borderRadius: 8,
+ background: selectedId === a.appeal_id ? "#e3f2fd" : "#fff",
+ border: `2px solid ${a.status === 1 ? "#43a047" : "#e53935"}`,
+ color: a.status === 1 ? "#43a047" : "#e53935",
+ fontWeight: 600,
+ cursor: "pointer",
+ boxShadow: selectedId === a.appeal_id ? "0 2px 8px #b2d8ea" : "none",
+ transition: "all 0.2s"
+ }}
+ >
+ {a.appeal_id}
+ <span style={{
+ float: "right",
+ fontSize: 12,
+ color: a.status === 1 ? "#43a047" : "#e53935"
+ }}>
+ {a.status === 1 ? "已审核" : "未审核"}
+ </span>
+ </div>
+ ))}
+ </div>
+ </div>
+ {/* 申诉详情 */}
+ <div style={{ flex: 1, padding: "40px 48px" }}>
+ <h2 style={{ marginBottom: 24, color: "#1976d2" }}>申诉详情</h2>
+ <div style={{ background: "#fff", borderRadius: 12, padding: 32, boxShadow: "0 2px 8px #e0e7ff", marginBottom: 32 }}>
+ <div style={{ marginBottom: 18 }}>
+ <b>申诉ID：</b>{selectedAppeal.appeal_id}
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>用户ID：</b>{selectedAppeal.user_id}
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>申诉内容：</b>{selectedAppeal.content}
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>申诉文件：</b>
+ <FileViewer url={selectedAppeal.file_url} />
+ </div>
+ </div>
+ {/* 审核按钮 */}
+ <div style={{ display: "flex", gap: 32, justifyContent: "center" }}>
+ <button
+ style={{
+ background: selectedAppeal.status === 1 ? "#bdbdbd" : "#43a047",
+ color: "#fff",
+ border: "none",
+ borderRadius: 8,
+ padding: "10px 38px",
+ fontWeight: 600,
+ fontSize: 18,
+ cursor: selectedAppeal.status === 1 ? "not-allowed" : "pointer"
+ }}
+ disabled={selectedAppeal.status === 1}
+ onClick={handleApprove}
+ >
+ 通过
+ </button>
+ <button
+ style={{
+ background: selectedAppeal.status === 1 ? "#bdbdbd" : "#e53935",
+ color: "#fff",
+ border: "none",
+ borderRadius: 8,
+ padding: "10px 38px",
+ fontWeight: 600,
+ fontSize: 18,
+ cursor: selectedAppeal.status === 1 ? "not-allowed" : "pointer"
+ }}
+ disabled={selectedAppeal.status === 1}
+ onClick={handleReject}
+ >
+ 不通过
+ </button>
+ </div>
+ </div>
+ </div>
+ );
+}
\ No newline at end of file
diff --git a/front/src/LoginPage.js b/front/src/LoginPage.js
index 5bb3603..890b690 100644
--- a/front/src/LoginPage.js
+++ b/front/src/LoginPage.js
@@ -15,6 +15,12 @@
};
const handleLogin = async () => {
+ // 进入管理员页面
+ if (formData.username === "admin" && formData.password === "admin123") {
+ navigate('/admin');
+ return;
+ }
+
if (formData.password.length < 8) {
setErrorMessage('密码必须至少包含八位字符！');
return;
@@ -60,7 +66,7 @@
const { username, password } = JSON.parse(regUser);
setFormData({ username, password });
sessionStorage.removeItem('registeredUser');
- } catch {}
+ } catch { }
}
}, []);
diff --git a/front/src/MigrationPage.js b/front/src/MigrationPage.js
new file mode 100644
index 0000000..ed88a55
--- /dev/null
+++ b/front/src/MigrationPage.js
@@ -0,0 +1,152 @@
+import React, { useState } from "react";
+
+// 示例迁移数据
+const migrations = [
+ {
+ migration_id: "m001",
+ user_id: "u001",
+ application_url: "http://sse.bjtu.edu.cn/media/attachments/2024/10/20241012160658.pdf",
+ approved: 0,
+ pending_magic: 10,
+ granted_magic: 0,
+ pending_uploaded: 1000,
+ granted_uploaded: 0,
+ },
+ {
+ migration_id: "m002",
+ user_id: "u002",
+ application_url: "http://sse.bjtu.edu.cn/media/attachments/2024/10/20241012160658.pdf",
+ approved: 1,
+ pending_magic: 20,
+ granted_magic: 20,
+ pending_uploaded: 2000,
+ granted_uploaded: 2000,
+ },
+];
+
+// 简单PDF预览组件
+function FileViewer({ url }) {
+ if (!url) return <div>无附件</div>;
+ if (url.endsWith(".pdf")) {
+ return (
+ <iframe
+ src={url}
+ title="PDF预览"
+ width="100%"
+ height="400px"
+ style={{ border: "1px solid #ccc", borderRadius: 8 }}
+ />
+ );
+ }
+ // 这里只做PDF示例，实际可扩展为DOC等
+ return <a href={url} target="_blank" rel="noopener noreferrer">下载附件</a>;
+}
+
+export default function MigrationPage() {
+ const [selectedId, setSelectedId] = useState(migrations[0].migration_id);
+ const selectedMigration = migrations.find(m => m.migration_id === selectedId);
+
+ const handleApprove = () => {
+ alert("已通过迁移（示例，无实际状态变更）");
+ };
+ const handleReject = () => {
+ alert("已拒绝迁移（示例，无实际状态变更）");
+ };
+
+ return (
+ <div style={{ display: "flex", minHeight: "100vh", background: "#f7faff" }}>
+ {/* 侧栏 */}
+ <div style={{ width: 180, background: "#fff", borderRight: "1px solid #e0e7ff", padding: 0 }}>
+ <h3 style={{ textAlign: "center", padding: "18px 0 0 0", color: "#1976d2" }}>迁移列表</h3>
+ <div style={{ display: "flex", flexDirection: "column", gap: 12, marginTop: 18 }}>
+ {migrations.map(m => (
+ <div
+ key={m.migration_id}
+ onClick={() => setSelectedId(m.migration_id)}
+ style={{
+ margin: "0 12px",
+ padding: "16px 10px",
+ borderRadius: 8,
+ background: selectedId === m.migration_id ? "#e3f2fd" : "#fff",
+ border: `2px solid ${m.approved === 1 ? "#43a047" : "#e53935"}`,
+ color: m.approved === 1 ? "#43a047" : "#e53935",
+ fontWeight: 600,
+ cursor: "pointer",
+ boxShadow: selectedId === m.migration_id ? "0 2px 8px #b2d8ea" : "none",
+ transition: "all 0.2s"
+ }}
+ >
+ {m.migration_id}
+ <span style={{
+ float: "right",
+ fontSize: 12,
+ color: m.approved === 1 ? "#43a047" : "#e53935"
+ }}>
+ {m.approved === 1 ? "已审核" : "未审核"}
+ </span>
+ </div>
+ ))}
+ </div>
+ </div>
+ {/* 迁移详情 */}
+ <div style={{ flex: 1, padding: "40px 48px" }}>
+ <h2 style={{ marginBottom: 24, color: "#1976d2" }}>迁移详情</h2>
+ <div style={{ background: "#fff", borderRadius: 12, padding: 32, boxShadow: "0 2px 8px #e0e7ff", marginBottom: 32 }}>
+ <div style={{ marginBottom: 18 }}>
+ <b>迁移ID：</b>{selectedMigration.migration_id}
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>用户ID：</b>{selectedMigration.user_id}
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>申请文件：</b>
+ <FileViewer url={selectedMigration.application_url} />
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>待迁移魔法值：</b>{selectedMigration.pending_magic}，
+ <b>已迁移魔法值：</b>{selectedMigration.granted_magic}
+ </div>
+ <div style={{ marginBottom: 18 }}>
+ <b>待迁移上传量：</b>{selectedMigration.pending_uploaded}，
+ <b>已迁移上传量：</b>{selectedMigration.granted_uploaded}
+ </div>
+ </div>
+ {/* 审核按钮 */}
+ <div style={{ display: "flex", gap: 32, justifyContent: "center" }}>
+ <button
+ style={{
+ background: selectedMigration.approved === 1 ? "#bdbdbd" : "#43a047",
+ color: "#fff",
+ border: "none",
+ borderRadius: 8,
+ padding: "10px 38px",
+ fontWeight: 600,
+ fontSize: 18,
+ cursor: selectedMigration.approved === 1 ? "not-allowed" : "pointer"
+ }}
+ disabled={selectedMigration.approved === 1}
+ onClick={handleApprove}
+ >
+ 通过
+ </button>
+ <button
+ style={{
+ background: selectedMigration.approved === 1 ? "#bdbdbd" : "#e53935",
+ color: "#fff",
+ border: "none",
+ borderRadius: 8,
+ padding: "10px 38px",
+ fontWeight: 600,
+ fontSize: 18,
+ cursor: selectedMigration.approved === 1 ? "not-allowed" : "pointer"
+ }}
+ disabled={selectedMigration.approved === 1}
+ onClick={handleReject}
+ >
+ 不通过
+ </button>
+ </div>
+ </div>
+ </div>
+ );
+}
\ No newline at end of file
diff --git a/front/src/UserProfile.js b/front/src/UserProfile.js
index 753e901..16fdcd2 100644
--- a/front/src/UserProfile.js
+++ b/front/src/UserProfile.js
@@ -33,7 +33,7 @@
if (!userid) return;
try {
const res = await fetch(`${API_BASE_URL}/api/user-profile?userid=${userid}`);
-
+
if (res.ok) {
const data = await res.json();
setUserInfo(data);
@@ -91,9 +91,9 @@
};
const handleSave = async () => {
- if (tempUserInfo.gender === "男性"){
+ if (tempUserInfo.gender === "男性") {
tempUserInfo.gender = "m";
- }else if (tempUserInfo.gender === "女性"){
+ } else if (tempUserInfo.gender === "女性") {
tempUserInfo.gender = "f";
}
setUserInfo({ ...tempUserInfo });
@@ -222,7 +222,7 @@
>
{tempUserInfo.gender === 'm' ? '男性'
: tempUserInfo.gender === 'f' ? '女性'
- : '性别'}
+ : '性别'}
</button>
{tempUserInfo.showGenderOptions && (
<ul
@@ -307,7 +307,7 @@
// const userid = localStorage.getItem("userid");
// const userid = "550e8400-e29b-41d4-a716-446655440000"; // 示例userid
try {
-
+
const res = await fetch(`${API_BASE_URL}/api/delete-seed`, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
diff --git a/recommend/hello.py b/recommend/hello.py
deleted file mode 100644
index c6d4e16..0000000
--- a/recommend/hello.py
+++ /dev/null
@@ -1 +0,0 @@
-print("Hello G10!")
diff --git a/recommend/inference.py b/recommend/inference.py
new file mode 100644
index 0000000..697b569
--- /dev/null
+++ b/recommend/inference.py
@@ -0,0 +1,54 @@
+import sys
+sys.path.append('./')
+
+from os import path
+from utils.parse_args import args
+from utils.dataloader import EdgeListData
+from model.LightGCN import LightGCN
+import torch
+import numpy as np
+import time
+
+# 计时：脚本开始
+t_start = time.time()
+
+# 配置参数
+args.data_path = './'
+args.device = 'cuda:7'
+args.pre_model_path = './model/LightGCN_pretrained.pt'
+
+# 1. 加载数据集
+t_data_start = time.time()
+pretrain_data = path.join(args.data_path, "uig.txt")
+pretrain_val_data = path.join(args.data_path, "uig.txt")
+dataset = EdgeListData(pretrain_data, pretrain_val_data)
+t_data_end = time.time()
+
+
+# 2. 加载LightGCN模型
+pretrained_dict = torch.load(args.pre_model_path, map_location=args.device, weights_only=True)
+pretrained_dict['user_embedding'] = pretrained_dict['user_embedding'][:dataset.num_users]
+pretrained_dict['item_embedding'] = pretrained_dict['item_embedding'][:dataset.num_items]
+
+model = LightGCN(dataset, phase='vanilla').to(args.device)
+model.load_state_dict(pretrained_dict, strict=False)
+model.eval()
+
+# 3. 输入用户ID
+user_id = 1
+
+# 4. 推理：获取embedding并打分
+t_infer_start = time.time()
+with torch.no_grad():
+ user_emb, item_emb = model.generate()
+ user_vec = user_emb[user_id].unsqueeze(0)
+ scores = model.rating(user_vec, item_emb).squeeze(0)
+ pred_item = torch.argmax(scores).item()
+t_infer_end = time.time()
+
+t_end = time.time()
+
+print(f"用户{user_id}下一个最可能点击的物品ID为: {pred_item}")
+print(f"加载数据集耗时: {t_data_end - t_data_start:.4f} 秒")
+print(f"推理耗时: {t_infer_end - t_infer_start:.4f} 秒")
+print(f"脚本总耗时: {t_end - t_start:.4f} 秒")
\ No newline at end of file
diff --git a/recommend/model/LightGCN.py b/recommend/model/LightGCN.py
new file mode 100644
index 0000000..b6b447e
--- /dev/null
+++ b/recommend/model/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 utils.parse_args import args
+from model.base_model import BaseModel
+from model.operators import EdgelistDrop
+from model.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
diff --git a/recommend/model/LightGCN_pretrained.pt b/recommend/model/LightGCN_pretrained.pt
new file mode 100644
index 0000000..825e0e2
--- /dev/null
+++ b/recommend/model/LightGCN_pretrained.pt
Binary files differ
diff --git a/recommend/model/base_model.py b/recommend/model/base_model.py
new file mode 100644
index 0000000..819442a
--- /dev/null
+++ b/recommend/model/base_model.py
@@ -0,0 +1,111 @@
+import torch
+import torch.nn as nn
+from utils.parse_args import args
+from scipy.sparse import csr_matrix
+import scipy.sparse as sp
+import numpy as np
+import torch.nn.functional as F
+
+
+class BaseModel(nn.Module):
+ def __init__(self, dataloader):
+ super(BaseModel, self).__init__()
+ self.num_users = dataloader.num_users
+ self.num_items = dataloader.num_items
+ self.emb_size = args.emb_size
+
+ def forward(self):
+ pass
+
+ def cal_loss(self, batch_data):
+ pass
+
+ def _check_inf(self, loss, pos_score, neg_score, edge_weight):
+ # find inf idx
+ inf_idx = torch.isinf(loss) | torch.isnan(loss)
+ if inf_idx.any():
+ print("find inf in loss")
+ if type(edge_weight) != int:
+ print(edge_weight[inf_idx])
+ print(f"pos_score: {pos_score[inf_idx]}")
+ print(f"neg_score: {neg_score[inf_idx]}")
+ raise ValueError("find inf in loss")
+
+ def _make_binorm_adj(self, mat):
+ a = csr_matrix((self.num_users, self.num_users))
+ b = csr_matrix((self.num_items, self.num_items))
+ mat = sp.vstack(
+ [sp.hstack([a, mat]), sp.hstack([mat.transpose(), b])])
+ mat = (mat != 0) * 1.0
+ # mat = (mat + sp.eye(mat.shape[0])) * 1.0# MARK
+ degree = np.array(mat.sum(axis=-1))
+ d_inv_sqrt = np.reshape(np.power(degree, -0.5), [-1])
+ d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0
+ d_inv_sqrt_mat = sp.diags(d_inv_sqrt)
+ mat = mat.dot(d_inv_sqrt_mat).transpose().dot(
+ d_inv_sqrt_mat).tocoo()
+
+ # make torch tensor
+ idxs = torch.from_numpy(np.vstack([mat.row, mat.col]).astype(np.int64))
+ vals = torch.from_numpy(mat.data.astype(np.float32))
+ shape = torch.Size(mat.shape)
+ return torch.sparse.FloatTensor(idxs, vals, shape).to(args.device)
+
+ def _make_binorm_adj_self_loop(self, mat):
+ a = csr_matrix((self.num_users, self.num_users))
+ b = csr_matrix((self.num_items, self.num_items))
+ mat = sp.vstack(
+ [sp.hstack([a, mat]), sp.hstack([mat.transpose(), b])])
+ mat = (mat != 0) * 1.0
+ mat = (mat + sp.eye(mat.shape[0])) * 1.0 # self loop
+ degree = np.array(mat.sum(axis=-1))
+ d_inv_sqrt = np.reshape(np.power(degree, -0.5), [-1])
+ d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0
+ d_inv_sqrt_mat = sp.diags(d_inv_sqrt)
+ mat = mat.dot(d_inv_sqrt_mat).transpose().dot(
+ d_inv_sqrt_mat).tocoo()
+
+ # make torch tensor
+ idxs = torch.from_numpy(np.vstack([mat.row, mat.col]).astype(np.int64))
+ vals = torch.from_numpy(mat.data.astype(np.float32))
+ shape = torch.Size(mat.shape)
+ return torch.sparse.FloatTensor(idxs, vals, shape).to(args.device)
+
+
+ def _sp_matrix_to_sp_tensor(self, sp_matrix):
+ coo = sp_matrix.tocoo()
+ indices = torch.LongTensor([coo.row, coo.col])
+ values = torch.FloatTensor(coo.data)
+ return torch.sparse.FloatTensor(indices, values, coo.shape).coalesce().to(args.device)
+
+ def _bpr_loss(self, user_emb, pos_item_emb, neg_item_emb):
+ pos_score = (user_emb * pos_item_emb).sum(dim=1)
+ neg_score = (user_emb * neg_item_emb).sum(dim=1)
+ loss = -torch.log(1e-10 + torch.sigmoid((pos_score - neg_score)))
+ self._check_inf(loss, pos_score, neg_score, 0)
+ return loss.mean()
+
+ def _nce_loss(self, pos_score, neg_score, edge_weight=1):
+ numerator = torch.exp(pos_score)
+ denominator = torch.exp(pos_score) + torch.exp(neg_score).sum(dim=1)
+ loss = -torch.log(numerator/denominator) * edge_weight
+ self._check_inf(loss, pos_score, neg_score, edge_weight)
+ return loss.mean()
+
+ def _infonce_loss(self, pos_1, pos_2, negs, tau):
+ pos_1 = self.cl_mlp(pos_1)
+ pos_2 = self.cl_mlp(pos_2)
+ negs = self.cl_mlp(negs)
+ pos_1 = F.normalize(pos_1, dim=-1)
+ pos_2 = F.normalize(pos_2, dim=-1)
+ negs = F.normalize(negs, dim=-1)
+ pos_score = torch.mul(pos_1, pos_2).sum(dim=1)
+ # B, 1, E * B, E, N -> B, N
+ neg_score = torch.bmm(pos_1.unsqueeze(1), negs.transpose(1, 2)).squeeze(1)
+ # infonce loss
+ numerator = torch.exp(pos_score / tau)
+ denominator = torch.exp(pos_score / tau) + torch.exp(neg_score / tau).sum(dim=1)
+ loss = -torch.log(numerator/denominator)
+ self._check_inf(loss, pos_score, neg_score, 0)
+ return loss.mean()
+
\ No newline at end of file
diff --git a/recommend/model/operators.py b/recommend/model/operators.py
new file mode 100644
index 0000000..a508966
--- /dev/null
+++ b/recommend/model/operators.py
@@ -0,0 +1,52 @@
+import torch
+from typing import Optional, Tuple
+from torch import nn
+
+def broadcast(src: torch.Tensor, other: torch.Tensor, dim: int):
+ if dim < 0:
+ dim = other.dim() + dim
+ if src.dim() == 1:
+ for _ in range(0, dim):
+ src = src.unsqueeze(0)
+ for _ in range(src.dim(), other.dim()):
+ src = src.unsqueeze(-1)
+ src = src.expand(other.size())
+ return src
+
+def scatter_sum(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
+ out: Optional[torch.Tensor] = None,
+ dim_size: Optional[int] = None) -> torch.Tensor:
+ index = broadcast(index, src, dim)
+ if out is None:
+ size = list(src.size())
+ if dim_size is not None:
+ size[dim] = dim_size
+ elif index.numel() == 0:
+ size[dim] = 0
+ else:
+ size[dim] = int(index.max()) + 1
+ out = torch.zeros(size, dtype=src.dtype, device=src.device)
+ return out.scatter_add_(dim, index, src)
+ else:
+ return out.scatter_add_(dim, index, src)
+
+def scatter_add(src: torch.Tensor, index: torch.Tensor, dim: int = -1,
+ out: Optional[torch.Tensor] = None,
+ dim_size: Optional[int] = None) -> torch.Tensor:
+ return scatter_sum(src, index, dim, out, dim_size)
+
+
+class EdgelistDrop(nn.Module):
+ def __init__(self):
+ super(EdgelistDrop, self).__init__()
+
+ def forward(self, edgeList, keep_rate, return_mask=False):
+ if keep_rate == 1.0:
+ return edgeList, torch.ones(edgeList.size(0)).type(torch.bool)
+ edgeNum = edgeList.size(0)
+ mask = (torch.rand(edgeNum) + keep_rate).floor().type(torch.bool)
+ newEdgeList = edgeList[mask, :]
+ if return_mask:
+ return newEdgeList, mask
+ else:
+ return newEdgeList
diff --git a/recommend/uig.txt b/recommend/uig.txt
new file mode 100644
index 0000000..5846057
--- /dev/null
+++ b/recommend/uig.txt
@@ -0,0 +1,2 @@
+0 1 3 9 12 5 7 6 8 4 1511683379 1511683385 1511683431 1511683453 1511683481 1511692992 1511693011 1511693077 1511787191
+1 10 11 2 1511578239 1511594732 1511664627
\ No newline at end of file
diff --git a/recommend/utils/dataloader.py b/recommend/utils/dataloader.py
new file mode 100644
index 0000000..d519f17
--- /dev/null
+++ b/recommend/utils/dataloader.py
@@ -0,0 +1,92 @@
+from utils.parse_args import args
+from os import path
+from tqdm import tqdm
+import numpy as np
+import scipy.sparse as sp
+import torch
+import networkx as nx
+from copy import deepcopy
+from collections import defaultdict
+import pandas as pd
+
+
+class EdgeListData:
+ def __init__(self, train_file, test_file, phase='pretrain', pre_dataset=None, has_time=True):
+ self.phase = phase
+ self.has_time = has_time
+ self.pre_dataset = pre_dataset
+
+ self.hour_interval = args.hour_interval_pre if phase == 'pretrain' else args.hour_interval_f
+
+ self.edgelist = []
+ self.edge_time = []
+ self.num_users = 0
+ self.num_items = 0
+ self.num_edges = 0
+
+ self.train_user_dict = {}
+ self.test_user_dict = {}
+
+ self._load_data(train_file, test_file, has_time)
+
+ if phase == 'pretrain':
+ self.user_hist_dict = self.train_user_dict
+
+ users_has_hist = set(list(self.user_hist_dict.keys()))
+ all_users = set(list(range(self.num_users)))
+ users_no_hist = all_users - users_has_hist
+ for u in users_no_hist:
+ self.user_hist_dict[u] = []
+
+ def _read_file(self, train_file, test_file, has_time=True):
+ with open(train_file, 'r') as f:
+ for line in f:
+ line = line.strip().split('\t')
+ if not has_time:
+ user, items = line[:2]
+ times = " ".join(["0"] * len(items.split(" ")))
+ else:
+ user, items, times = line
+
+ for i in items.split(" "):
+ self.edgelist.append((int(user), int(i)))
+ for i in times.split(" "):
+ self.edge_time.append(int(i))
+ self.train_user_dict[int(user)] = [int(i) for i in items.split(" ")]
+
+ self.test_edge_num = 0
+ with open(test_file, 'r') as f:
+ for line in f:
+ line = line.strip().split('\t')
+ user, items = line[:2]
+ self.test_user_dict[int(user)] = [int(i) for i in items.split(" ")]
+ self.test_edge_num += len(self.test_user_dict[int(user)])
+
+ def _load_data(self, train_file, test_file, has_time=True):
+ self._read_file(train_file, test_file, has_time)
+
+ self.edgelist = np.array(self.edgelist, dtype=np.int32)
+ self.edge_time = 1 + self.timestamp_to_time_step(np.array(self.edge_time, dtype=np.int32))
+ self.num_edges = len(self.edgelist)
+ if self.pre_dataset is not None:
+ self.num_users = self.pre_dataset.num_users
+ self.num_items = self.pre_dataset.num_items
+ else:
+ self.num_users = max([np.max(self.edgelist[:, 0]) + 1, np.max(list(self.test_user_dict.keys())) + 1])
+ self.num_items = max([np.max(self.edgelist[:, 1]) + 1, np.max([np.max(self.test_user_dict[u]) for u in self.test_user_dict.keys()]) + 1])
+
+ self.graph = sp.coo_matrix((np.ones(self.num_edges), (self.edgelist[:, 0], self.edgelist[:, 1])), shape=(self.num_users, self.num_items))
+
+ if self.has_time:
+ self.edge_time_dict = defaultdict(dict)
+ for i in range(len(self.edgelist)):
+ self.edge_time_dict[self.edgelist[i][0]][self.edgelist[i][1]+self.num_users] = self.edge_time[i]
+ self.edge_time_dict[self.edgelist[i][1]+self.num_users][self.edgelist[i][0]] = self.edge_time[i]
+
+ def timestamp_to_time_step(self, timestamp_arr, least_time=None):
+ interval_hour = self.hour_interval
+ if least_time is None:
+ least_time = np.min(timestamp_arr)
+ timestamp_arr = timestamp_arr - least_time
+ timestamp_arr = timestamp_arr // (interval_hour * 3600)
+ return timestamp_arr
diff --git a/recommend/utils/parse_args.py b/recommend/utils/parse_args.py
new file mode 100644
index 0000000..3e86a47
--- /dev/null
+++ b/recommend/utils/parse_args.py
@@ -0,0 +1,57 @@
+import argparse
+
+def parse_args():
+ parser = argparse.ArgumentParser(description='GraphPro')
+ parser.add_argument('--phase', type=str, default='pretrain')
+ parser.add_argument('--plugin', action='store_true', default=False)
+ parser.add_argument('--save_path', type=str, default="saved" ,help='where to save model and logs')
+ parser.add_argument('--data_path', type=str, default="dataset/yelp",help='where to load data')
+ parser.add_argument('--exp_name', type=str, default='1')
+ parser.add_argument('--desc', type=str, default='')
+ parser.add_argument('--ab', type=str, default='full')
+ parser.add_argument('--log', type=int, default=1)
+
+ parser.add_argument('--device', type=str, default="cuda")
+ parser.add_argument('--model', type=str, default='GraphPro')
+ parser.add_argument('--pre_model', type=str, default='GraphPro')
+ parser.add_argument('--f_model', type=str, default='GraphPro')
+ parser.add_argument('--pre_model_path', type=str, default='pretrained_model.pt')
+
+ parser.add_argument('--hour_interval_pre', type=float, default=1)
+ parser.add_argument('--hour_interval_f', type=int, default=1)
+ parser.add_argument('--emb_dropout', type=float, default=0)
+
+ parser.add_argument('--updt_inter', type=int, default=1)
+ parser.add_argument('--samp_decay', type=float, default=0.05)
+
+ parser.add_argument('--edge_dropout', type=float, default=0.5)
+ parser.add_argument('--emb_size', type=int, default=64)
+ parser.add_argument('--batch_size', type=int, default=2048)
+ parser.add_argument('--eval_batch_size', type=int, default=512)
+ parser.add_argument('--seed', type=int, default=2023)
+ parser.add_argument('--num_epochs', type=int, default=300)
+ parser.add_argument('--neighbor_sample_num', type=int, default=5)
+ parser.add_argument('--lr', type=float, default=0.001)
+ parser.add_argument('--weight_decay', type=float, default=1e-4)
+ parser.add_argument('--metrics', type=str, default='recall;ndcg')
+ parser.add_argument('--metrics_k', type=str, default='20')
+ parser.add_argument('--early_stop_patience', type=int, default=10)
+ parser.add_argument('--neg_num', type=int, default=1)
+
+ parser.add_argument('--num_layers', type=int, default=3)
+
+
+ return parser
+
+parser = parse_args()
+args = parser.parse_known_args()[0]
+if args.pre_model == args.f_model:
+ args.model = args.pre_model
+elif args.pre_model != 'LightGCN':
+ args.model = args.pre_model
+
+args = parser.parse_args()
+if args.pre_model == args.f_model:
+ args.model = args.pre_model
+elif args.pre_model != 'LightGCN':
+ args.model = args.pre_model
\ No newline at end of file