💖💖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
经典名著推荐系统设计与实现介绍
《经典名著推荐系统》是一个基于Hadoop+Spark大数据技术架构的智能推荐平台，专门为文学爱好者提供个性化的经典名著推荐服务。系统采用Python作为主要开发语言，后端框架选用Django，前端技术栈包含Vue+ElementUI+Echarts，数据存储采用MySQL数据库。系统的核心亮点在于运用Spark进行大规模数据处理和机器学习算法实现，通过分析用户的阅读历史、评分行为、浏览时长等多维度数据，结合协同过滤算法和内容相似度计算，为每个用户生成精准的个性化推荐列表。系统功能涵盖用户管理、经典名著信息展示、智能推荐算法、预测分析、系统管理和个人中心等模块，能够处理海量的图书数据和用户行为数据，通过Spark SQL进行复杂的数据查询和统计分析，利用Pandas和NumPy进行数据预处理和特征工程，最终通过直观的图表展示为用户呈现数据分析结果，帮助用户发现适合自己的经典文学作品。
经典名著推荐系统设计与实现演示视频
经典名著推荐系统设计与实现演示图片
经典名著推荐系统设计与实现代码展示
from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS
from pyspark.ml.feature import StringIndexer
from pyspark.sql.functions import col, desc, avg, count, when, isnan, isnull
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import mysql.connector
from sklearn.metrics.pairwise import cosine_similarity
spark = SparkSession.builder.appName("ClassicBookRecommendSystem").master("local[*]").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def book_recommendation_algorithm(user_id, num_recommendations=10):
connection = mysql.connector.connect(host='localhost', user='root', password='password', database='book_system')
cursor = connection.cursor()
cursor.execute("SELECT user_id, book_id, rating FROM user_ratings")
rating_data = cursor.fetchall()
rating_df = spark.createDataFrame(rating_data, ["user_id", "book_id", "rating"])
user_indexer = StringIndexer(inputCol="user_id", outputCol="user_index")
book_indexer = StringIndexer(inputCol="book_id", outputCol="book_index")
rating_df = user_indexer.fit(rating_df).transform(rating_df)
rating_df = book_indexer.fit(rating_df).transform(rating_df)
als = ALS(maxIter=10, regParam=0.1, userCol="user_index", itemCol="book_index", ratingCol="rating", coldStartStrategy="drop", nonnegative=True)
model = als.fit(rating_df)
user_subset = rating_df.filter(col("user_id") == user_id).select("user_index").distinct()
if user_subset.count() == 0:
cursor.execute("SELECT book_id, title, avg_rating FROM books ORDER BY avg_rating DESC, popularity DESC LIMIT %s", (num_recommendations,))
recommendations = cursor.fetchall()
return [{"book_id": rec[0], "title": rec[1], "predicted_rating": rec[2]} for rec in recommendations]
user_recommendations = model.recommendForUserSubset(user_subset, num_recommendations)
recommendations_list = user_recommendations.collect()[0]["recommendations"]
book_ids = [rec["book_index"] for rec in recommendations_list]
predicted_ratings = [rec["rating"] for rec in recommendations_list]
book_details = []
for i, book_index in enumerate(book_ids):
cursor.execute("SELECT book_id, title, author, category FROM books WHERE book_id = (SELECT book_id FROM book_index_mapping WHERE book_index = %s)", (book_index,))
book_info = cursor.fetchone()
if book_info:
book_details.append({"book_id": book_info[0], "title": book_info[1], "author": book_info[2], "category": book_info[3], "predicted_rating": round(predicted_ratings[i], 2)})
cursor.close()
connection.close()
return book_details
def user_behavior_analysis(user_id=None, time_range=30):
connection = mysql.connector.connect(host='localhost', user='root', password='password', database='book_system')
cursor = connection.cursor()
if user_id:
cursor.execute("SELECT user_id, book_id, action_type, action_time, duration FROM user_behaviors WHERE user_id = %s AND action_time >= DATE_SUB(NOW(), INTERVAL %s DAY)", (user_id, time_range))
else:
cursor.execute("SELECT user_id, book_id, action_type, action_time, duration FROM user_behaviors WHERE action_time >= DATE_SUB(NOW(), INTERVAL %s DAY)", (time_range,))
behavior_data = cursor.fetchall()
behavior_df = spark.createDataFrame(behavior_data, ["user_id", "book_id", "action_type", "action_time", "duration"])
reading_time_analysis = behavior_df.filter(col("action_type") == "read").groupBy("user_id", "book_id").agg(avg("duration").alias("avg_reading_time"), count("*").alias("reading_sessions"))
popular_books = behavior_df.filter(col("action_type").isin(["read", "favorite", "comment"])).groupBy("book_id").agg(count("*").alias("interaction_count")).orderBy(desc("interaction_count"))
user_preferences = behavior_df.filter(col("action_type") == "read").groupBy("user_id").agg(avg("duration").alias("avg_session_time"), count("*").alias("total_sessions"))
category_preferences = behavior_df.join(spark.sql("SELECT book_id, category FROM books"), "book_id", "inner").groupBy("user_id", "category").agg(count("*").alias("category_interactions")).orderBy("user_id", desc("category_interactions"))
time_pattern_analysis = behavior_df.withColumn("hour", spark.sql("hour(action_time)")).groupBy("user_id", "hour").agg(count("*").alias("hourly_activities")).orderBy("user_id", "hour")
reading_completion_rate = behavior_df.filter(col("action_type") == "read").groupBy("user_id", "book_id").agg(avg("duration").alias("avg_time"), count("*").alias("sessions")).withColumn("completion_score", when(col("avg_time") > 1800, 1.0).when(col("avg_time") > 900, 0.7).otherwise(0.3))
engagement_metrics = behavior_df.groupBy("user_id").pivot("action_type").agg(count("*")).fillna(0)
result_data = {"reading_time_stats": reading_time_analysis.toPandas().to_dict('records'), "popular_books": popular_books.limit(20).toPandas().to_dict('records'), "user_preferences": user_preferences.toPandas().to_dict('records'), "category_preferences": category_preferences.toPandas().to_dict('records'), "time_patterns": time_pattern_analysis.toPandas().to_dict('records'), "completion_rates": reading_completion_rate.toPandas().to_dict('records'), "engagement_metrics": engagement_metrics.toPandas().to_dict('records')}
cursor.close()
connection.close()
return result_data
def reading_trend_prediction(period_days=90, forecast_days=30):
connection = mysql.connector.connect(host='localhost', user='root', password='password', database='book_system')
cursor = connection.cursor()
cursor.execute("SELECT DATE(action_time) as action_date, book_id, COUNT(*) as daily_reads FROM user_behaviors WHERE action_type = 'read' AND action_time >= DATE_SUB(NOW(), INTERVAL %s DAY) GROUP BY DATE(action_time), book_id ORDER BY action_date", (period_days,))
daily_reading_data = cursor.fetchall()
reading_df = spark.createDataFrame(daily_reading_data, ["action_date", "book_id", "daily_reads"])
cursor.execute("SELECT book_id, title, category, author, publication_year FROM books")
books_data = cursor.fetchall()
books_df = spark.createDataFrame(books_data, ["book_id", "title", "category", "author", "publication_year"])
trend_analysis = reading_df.groupBy("action_date").agg(count("book_id").alias("unique_books_read"), avg("daily_reads").alias("avg_daily_reads"), spark.sql("sum(daily_reads)").alias("total_reads"))
category_trends = reading_df.join(books_df, "book_id", "inner").groupBy("action_date", "category").agg(spark.sql("sum(daily_reads)").alias("category_reads")).orderBy("action_date", "category")
author_popularity = reading_df.join(books_df, "book_id", "inner").groupBy("author").agg(spark.sql("sum(daily_reads)").alias("total_author_reads"), count("book_id").alias("books_count")).orderBy(desc("total_author_reads"))
seasonal_patterns = reading_df.withColumn("month", spark.sql("month(action_date)")).withColumn("weekday", spark.sql("dayofweek(action_date)")).groupBy("month", "weekday").agg(avg("daily_reads").alias("avg_reads_by_time"))
reading_velocity = reading_df.withColumn("days_since_start", spark.sql("datediff(action_date, (SELECT MIN(action_date) FROM reading_df))")).select("days_since_start", "daily_reads")
growth_rate_calculation = reading_df.orderBy("action_date").withColumn("prev_day_reads", spark.sql("lag(daily_reads) over (order by action_date)")).withColumn("growth_rate", (col("daily_reads") - col("prev_day_reads")) / col("prev_day_reads") * 100).filter(col("growth_rate").isNotNull())
predicted_trends = []
recent_avg = trend_analysis.orderBy(desc("action_date")).limit(7).agg(avg("total_reads")).collect()[0][0]
growth_avg = growth_rate_calculation.filter(col("growth_rate") > -50).agg(avg("growth_rate")).collect()[0][0] or 0
for i in range(forecast_days):
predicted_value = recent_avg * (1 + growth_avg/100) ** i
predicted_trends.append({"forecast_day": i+1, "predicted_reads": round(predicted_value, 2)})
user_engagement_prediction = reading_df.join(books_df, "book_id", "inner").groupBy("category").agg(avg("daily_reads").alias("avg_category_engagement")).orderBy(desc("avg_category_engagement"))
result = {"historical_trends": trend_analysis.toPandas().to_dict('records'), "category_trends": category_trends.toPandas().to_dict('records'), "author_popularity": author_popularity.limit(20).toPandas().to_dict('records'), "seasonal_patterns": seasonal_patterns.toPandas().to_dict('records'), "growth_analysis": growth_rate_calculation.toPandas().to_dict('records'), "predictions": predicted_trends, "engagement_by_category": user_engagement_prediction.toPandas().to_dict('records')}
cursor.close()
connection.close()
return result
经典名著推荐系统设计与实现文档展示
💖💖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
