图书推荐系统 | book

Multi-source book recommendation system.

A recommendation system fusing book tags, content and ratings — with data analysis, experiments and explainability.

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book is a multi-source book recommendation system — it fuses book tags, content and rating data for recommendation, with data preprocessing, analysis, experiments and explainability modules.

Note

中文项目：基于多源信息融合的书籍推荐系统。

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Quickstart

git clone https://github.com/Windyhhh/book.git
cd book

# install dependencies
python install_dependencies.py

# run the recommendation demo
python demo_recommendation_system.py

# run the full experiment
python main_experiment.py

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Features

• Multi-source fusion — tags, content, ratings.
• Recommendation demo — demo_recommendation_system.py.
• Explainability — explainability.py for interpretable recommendations.
• Evaluation — evaluation.py for accuracy.

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Project Structure

book/
├── demo_recommendation_system.py
├── main_experiment.py
├── data_analysis.py / data_preprocessing.py
├── evaluation.py / explainability.py
├── install_dependencies.py
└── books.csv / book_tags.csv

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技术实现细节

架构概览

项目采用模块化设计，核心目录包括：基于多源信息融合的书籍推荐系统研究。

核心类与模块

• DataAnalyzer
• DataPreprocessor
• SimpleBookRecommendationSystem

关键函数

• load_data, basic_statistics, data_quality_analysis, visualize_data_distribution, clean_books_data, process_tags_data, create_book_tag_matrix, create_content_features, create_user_item_matrix, prepare_content_features

技术栈与依赖

核心框架/库：NumPy, matplotlib, pandas, scikit-learn, seaborn

主要 import：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.feature_extraction.text import TfidfVectorizer
import warnings
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, LabelEncoder

实现要点

• 以 DataAnalyzer 为核心类，封装主要业务逻辑
• 通过 load_data 等函数实现核心流程编排
• 基于 NumPy, matplotlib, pandas 构建，技术栈成熟稳定
• 代码结构清晰，模块间低耦合，便于扩展和维护

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License

MIT — free to use, modify and distribute.