💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
基于深度学习的图书推荐系统介绍
本图书推荐系统是一个基于大数据技术栈构建的智能化图书管理平台，采用Hadoop+Spark作为大数据处理引擎，结合Python语言和Django框架进行后端开发，前端使用Vue+ElementUI+Echarts实现现代化的用户界面。系统不仅具备传统图书管理的基础功能，还通过Spark SQL和大数据分析技术实现了智能推荐算法，能够根据用户的借阅历史、浏览行为和偏好数据进行个性化图书推荐。平台包含完整的用户管理体系、图书分类与信息管理、借阅归还流程控制、库存管理、订单处理以及消息通知等核心模块。通过HDFS分布式文件系统存储海量图书数据，利用Spark进行实时数据处理和分析，结合MySQL数据库管理结构化信息，系统能够处理大规模用户群体的并发访问需求。整个架构采用前后端分离设计，后端提供RESTful API接口，前端通过Echarts展示数据分析结果，为图书馆管理者提供直观的运营数据统计和用户行为分析报告，同时为普通用户提供便捷的图书检索、借阅和个性化推荐服务。
基于深度学习的图书推荐系统演示视频
基于深度学习的图书推荐系统演示图片
基于深度学习的图书推荐系统代码展示
from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS
from pyspark.ml.feature import StringIndexer
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
import pandas as pd
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("BookRecommendationSystem").config("spark.sql.adaptive.enabled", "true").config("spark.serializer", "org.apache.spark.serializer.KryoSerializer").getOrCreate()
def generate_book_recommendations(user_id, num_recommendations=10):
user_ratings_df = spark.sql(f"SELECT user_id, book_id, rating FROM user_ratings WHERE user_id = {user_id}")
if user_ratings_df.count() == 0:
popular_books = spark.sql("SELECT book_id, COUNT(*) as borrow_count FROM borrow_records GROUP BY book_id ORDER BY borrow_count DESC LIMIT 10")
recommendations = popular_books.select("book_id").rdd.map(lambda x: x[0]).collect()
return recommendations
all_ratings_df = spark.sql("SELECT user_id, book_id, CAST(rating as float) as rating FROM user_ratings")
user_indexer = StringIndexer(inputCol="user_id", outputCol="user_index")
book_indexer = StringIndexer(inputCol="book_id", outputCol="book_index")
user_indexed = user_indexer.fit(all_ratings_df).transform(all_ratings_df)
final_df = book_indexer.fit(user_indexed).transform(user_indexed)
als = ALS(maxIter=10, regParam=0.1, userCol="user_index", itemCol="book_index", ratingCol="rating", coldStartStrategy="drop")
model = als.fit(final_df)
user_df = final_df.filter(final_df.user_id == user_id).select("user_index").distinct()
user_recs = model.recommendForUserSubset(user_df, num_recommendations)
recommendations_list = user_recs.collect()[0]['recommendations']
book_ids = [rec['book_index'] for rec in recommendations_list]
book_mapping = final_df.select("book_id", "book_index").distinct().collect()
book_index_to_id = {row['book_index']: row['book_id'] for row in book_mapping}
final_recommendations = [book_index_to_id.get(book_index) for book_index in book_ids if book_index_to_id.get(book_index)]
return final_recommendations
def process_book_borrow(user_id, book_id, borrow_days=30):
current_time = datetime.now()
return_date = current_time + timedelta(days=borrow_days)
book_info = spark.sql(f"SELECT * FROM books WHERE book_id = {book_id} AND stock_quantity > 0")
if book_info.count() == 0:
return {"status": "error", "message": "图书库存不足或不存在"}
user_active_borrows = spark.sql(f"SELECT COUNT(*) as count FROM borrow_records WHERE user_id = {user_id} AND return_status = 0")
if user_active_borrows.collect()[0]['count'] >= 5:
return {"status": "error", "message": "借阅数量已达上限"}
user_overdue = spark.sql(f"SELECT COUNT(*) as count FROM borrow_records WHERE user_id = {user_id} AND return_status = 0 AND expected_return_date < '{current_time}'")
if user_overdue.collect()[0]['count'] > 0:
return {"status": "error", "message": "存在逾期图书，请先归还"}
borrow_record_data = [(user_id, book_id, current_time.strftime('%Y-%m-%d %H:%M:%S'), return_date.strftime('%Y-%m-%d %H:%M:%S'), 0)]
borrow_df = spark.createDataFrame(borrow_record_data, ["user_id", "book_id", "borrow_date", "expected_return_date", "return_status"])
borrow_df.write.mode("append").saveAsTable("borrow_records")
spark.sql(f"UPDATE books SET stock_quantity = stock_quantity - 1, borrow_count = borrow_count + 1 WHERE book_id = {book_id}")
user_behavior_data = [(user_id, book_id, "borrow", current_time.strftime('%Y-%m-%d %H:%M:%S'))]
behavior_df = spark.createDataFrame(user_behavior_data, ["user_id", "book_id", "action_type", "action_time"])
behavior_df.write.mode("append").saveAsTable("user_behavior_logs")
return {"status": "success", "message": "借阅成功", "return_date": return_date.strftime('%Y-%m-%d')}
def analyze_user_reading_behavior(user_id=None, days_range=30):
end_date = datetime.now()
start_date = end_date - timedelta(days=days_range)
date_filter = f"action_time >= '{start_date.strftime('%Y-%m-%d')}' AND action_time <= '{end_date.strftime('%Y-%m-%d')}'"
user_filter = f"user_id = {user_id}" if user_id else "1=1"
behavior_stats = spark.sql(f"""
SELECT user_id, action_type, COUNT(*) as action_count,
COUNT(DISTINCT book_id) as unique_books,
AVG(CASE WHEN action_type = 'borrow' THEN 1 ELSE 0 END) as borrow_rate
FROM user_behavior_logs
WHERE {date_filter} AND {user_filter}
GROUP BY user_id, action_type
""")
reading_preferences = spark.sql(f"""
SELECT ub.user_id, b.category_id, bc.category_name, COUNT(*) as interaction_count
FROM user_behavior_logs ub
JOIN books b ON ub.book_id = b.book_id
JOIN book_categories bc ON b.category_id = bc.category_id
WHERE {date_filter} AND {user_filter}
GROUP BY ub.user_id, b.category_id, bc.category_name
ORDER BY interaction_count DESC
""")
active_hours = spark.sql(f"""
SELECT user_id, HOUR(action_time) as hour_of_day, COUNT(*) as activity_count
FROM user_behavior_logs
WHERE {date_filter} AND {user_filter}
GROUP BY user_id, HOUR(action_time)
ORDER BY activity_count DESC
""")
behavior_summary = behavior_stats.toPandas().to_dict('records')
preference_summary = reading_preferences.toPandas().to_dict('records')
activity_pattern = active_hours.toPandas().to_dict('records')
analysis_result = {
"behavior_stats": behavior_summary,
"reading_preferences": preference_summary,
"activity_patterns": activity_pattern,
"analysis_period": f"{start_date.strftime('%Y-%m-%d')} 到 {end_date.strftime('%Y-%m-%d')}"
}
return analysis_result
基于深度学习的图书推荐系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
