💖💖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
宁波旅游推荐周边商城实现与设计介绍
宁波旅游推荐周边商城实现与设计是一个融合大数据技术的智能化电商平台，专门针对宁波地区旅游市场需求而开发。该系统以Hadoop分布式存储框架为基础，结合Spark大数据处理引擎，实现了对用户行为数据、商品销售数据、景点访问数据的深度挖掘与分析。系统采用前后端分离架构，后端基于SpringBoot框架构建RESTful API接口，前端使用Vue+ElementUI打造响应式用户界面，通过Echarts实现数据可视化展示。核心功能涵盖用户管理、商品分类管理、商品信息展示、宁波景点推荐、订单处理、个人中心等模块，特别是在商品推荐算法方面，系统运用Spark SQL对用户浏览记录、购买历史、评价反馈等多维度数据进行实时分析，生成个性化推荐结果。同时，系统对宁波各大景点的访问热度、用户评价、周边商品销售情况进行综合分析，为游客提供精准的旅游商品推荐服务，有效提升用户购物体验和商家销售转化率。
宁波旅游推荐周边商城实现与设计演示视频
宁波旅游推荐周边商城实现与设计演示图片
宁波旅游推荐周边商城实现与设计代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, desc, when, sum as spark_sum
from pyspark.ml.recommendation import ALS
from pyspark.ml.feature import StringIndexer
import pandas as pd
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("NingboTourismRecommendation").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_user_behavior_and_recommend(user_id, limit=10):
user_behavior_df = spark.sql("""
SELECT user_id, product_id, view_count, purchase_count, rating
FROM user_behavior_log
WHERE created_date >= date_sub(current_date(), 30)
""")
user_purchase_df = spark.sql("""
SELECT user_id, product_id, category_id, purchase_amount, purchase_time
FROM order_details od
JOIN products p ON od.product_id = p.id
WHERE od.user_id = {}
""".format(user_id))
similar_users = user_behavior_df.filter(col("user_id") != user_id).groupBy("user_id").agg(
count("product_id").alias("total_products"),
avg("rating").alias("avg_rating")
).filter(col("avg_rating") > 4.0).orderBy(desc("total_products")).limit(50)
user_categories = user_purchase_df.groupBy("category_id").agg(
count("product_id").alias("purchase_count"),
avg("purchase_amount").alias("avg_amount")
).orderBy(desc("purchase_count"))
top_category = user_categories.first()["category_id"] if user_categories.count() > 0 else 1
als_model_data = user_behavior_df.select("user_id", "product_id", "rating").na.drop()
user_indexer = StringIndexer(inputCol="user_id", outputCol="user_index")
product_indexer = StringIndexer(inputCol="product_id", outputCol="product_index")
indexed_data = user_indexer.fit(als_model_data).transform(als_model_data)
indexed_data = product_indexer.fit(indexed_data).transform(indexed_data)
als = ALS(maxIter=10, regParam=0.1, userCol="user_index", itemCol="product_index", ratingCol="rating", coldStartStrategy="drop")
model = als.fit(indexed_data)
user_subset = indexed_data.select("user_index").filter(col("user_id") == user_id).distinct()
recommendations = model.recommendForUserSubset(user_subset, limit)
final_recommendations = spark.sql("""
SELECT p.id, p.name, p.price, p.category_id, p.image_url, p.description
FROM products p
WHERE p.category_id = {} AND p.status = 1
ORDER BY p.sales_count DESC, p.rating DESC
LIMIT {}
""".format(top_category, limit))
return final_recommendations.collect()
def analyze_scenic_spot_popularity():
spot_visit_df = spark.sql("""
SELECT spot_id, COUNT(DISTINCT user_id) as visitor_count,
AVG(rating) as avg_rating, COUNT(*) as total_visits
FROM spot_visit_logs
WHERE visit_date >= date_sub(current_date(), 7)
GROUP BY spot_id
""")
product_relation_df = spark.sql("""
SELECT s.id as spot_id, s.name as spot_name, p.id as product_id,
p.name as product_name, COUNT(od.id) as related_sales
FROM scenic_spots s
JOIN spot_products sp ON s.id = sp.spot_id
JOIN products p ON sp.product_id = p.id
JOIN order_details od ON p.id = od.product_id
WHERE od.created_date >= date_sub(current_date(), 30)
GROUP BY s.id, s.name, p.id, p.name
""")
weather_impact = spark.sql("""
SELECT spot_id, weather_type, COUNT(*) as visit_count,
AVG(CASE WHEN weather_type = 'sunny' THEN 1.2
WHEN weather_type = 'cloudy' THEN 1.0
ELSE 0.8 END) as weather_factor
FROM spot_visit_logs svl
JOIN weather_data wd ON svl.visit_date = wd.date
WHERE svl.visit_date >= date_sub(current_date(), 30)
GROUP BY spot_id, weather_type
""")
comprehensive_analysis = spot_visit_df.join(weather_impact, "spot_id", "left").select(
col("spot_id"),
col("visitor_count"),
col("avg_rating"),
col("total_visits"),
(col("visitor_count") * col("avg_rating") * col("weather_factor")).alias("popularity_score")
).orderBy(desc("popularity_score"))
peak_hours = spark.sql("""
SELECT spot_id, HOUR(visit_time) as visit_hour, COUNT(*) as hourly_visits
FROM spot_visit_logs
WHERE visit_date >= date_sub(current_date(), 7)
GROUP BY spot_id, HOUR(visit_time)
ORDER BY spot_id, hourly_visits DESC
""")
result_data = comprehensive_analysis.join(product_relation_df, "spot_id", "left").groupBy(
"spot_id", "visitor_count", "avg_rating", "popularity_score"
).agg(spark_sum("related_sales").alias("total_related_sales"))
return result_data.orderBy(desc("popularity_score")).limit(20).collect()
def process_order_data_with_spark():
daily_orders = spark.sql("""
SELECT DATE(created_date) as order_date, COUNT(*) as order_count,
SUM(total_amount) as daily_revenue, AVG(total_amount) as avg_order_value
FROM orders
WHERE created_date >= date_sub(current_date(), 30)
GROUP BY DATE(created_date)
ORDER BY order_date DESC
""")
user_order_patterns = spark.sql("""
SELECT user_id, COUNT(*) as total_orders, SUM(total_amount) as total_spent,
AVG(total_amount) as avg_spent, MIN(created_date) as first_order,
MAX(created_date) as last_order
FROM orders
WHERE status IN ('completed', 'shipped')
GROUP BY user_id
""")
product_performance = spark.sql("""
SELECT p.id, p.name, p.category_id, COUNT(od.id) as order_frequency,
SUM(od.quantity) as total_sold, SUM(od.subtotal) as total_revenue,
AVG(r.rating) as avg_product_rating
FROM products p
JOIN order_details od ON p.id = od.product_id
JOIN orders o ON od.order_id = o.id
LEFT JOIN reviews r ON p.id = r.product_id
WHERE o.created_date >= date_sub(current_date(), 90)
GROUP BY p.id, p.name, p.category_id
""")
refund_analysis = spark.sql("""
SELECT product_id, COUNT(*) as refund_count, AVG(refund_amount) as avg_refund,
refund_reason, COUNT(DISTINCT user_id) as affected_users
FROM refund_records
WHERE refund_date >= date_sub(current_date(), 60)
GROUP BY product_id, refund_reason
""")
seasonal_trends = spark.sql("""
SELECT MONTH(created_date) as order_month, category_id,
COUNT(*) as monthly_orders, SUM(total_amount) as monthly_revenue
FROM orders o
JOIN order_details od ON o.id = od.order_id
JOIN products p ON od.product_id = p.id
WHERE YEAR(created_date) = YEAR(current_date())
GROUP BY MONTH(created_date), category_id
ORDER BY order_month, monthly_revenue DESC
""")
inventory_optimization = product_performance.join(refund_analysis, "product_id", "left").select(
col("id"), col("name"), col("total_sold"), col("total_revenue"),
col("avg_product_rating"), col("refund_count"),
when(col("total_sold") > 100, "high_demand")
.when(col("total_sold") > 50, "medium_demand")
.otherwise("low_demand").alias("demand_level")
)
final_report = daily_orders.join(seasonal_trends, daily_orders.order_date.substr(6, 2) == seasonal_trends.order_month, "left")
return {
'daily_summary': daily_orders.collect(),
'user_patterns': user_order_patterns.orderBy(desc("total_spent")).limit(100).collect(),
'product_performance': inventory_optimization.orderBy(desc("total_revenue")).collect(),
'seasonal_analysis': seasonal_trends.collect()
}
宁波旅游推荐周边商城实现与设计文档展示
💖💖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
