一、个人简介
- 💖💖作者：计算机编程果茶熊
- 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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
- 计算机毕业设计选题
- 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
- 后端开发语言：Java+Python（两个版本都支持）
- 后端框架：Spring Boot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持）
- 前端：uni-app+微信小程序+安卓
- 数据库：MySQL
- 系统架构：C/S + B/S
- 开发工具：IDEA（Java的）或者PyCharm（Python的）+微信小程序开发工具
该个性化服装搭配推荐小程序是一款基于SpringBoot框架开发的智能服装推荐系统，采用uni-app技术构建微信小程序前端，通过MySQL数据库存储用户信息、商品数据和搭配记录。系统核心功能包括用户个人信息管理、服装商品信息展示、个性化搭配推荐、用户反馈收集、社区论坛交流等模块。平台运用协同过滤算法和内容推荐算法，根据用户的购买历史、浏览行为、风格偏好等多维度数据，为用户推荐符合个人审美和需求的服装搭配方案。系统支持商品分类管理、风格标签管理、搭配反馈统计、举报处理、充值消费记录等后台管理功能，为用户提供完整的服装购买和搭配体验闭环。通过数据挖掘技术分析用户行为模式，不断优化推荐算法的准确性和实用性，帮助用户解决日常穿搭难题，提升购物体验和搭配满意度。
三、视频解说
个性化推荐成热门趋势：SpringBoot服装搭配推荐小程序抓住2026毕设风口
四、部分功能展示
五、部分代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class PersonalizedRecommendationService {
@Autowired
private UserBehaviorMapper userBehaviorMapper;
@Autowired
private ClothingMapper clothingMapper;
private SparkSession spark = SparkSession.builder().appName("ClothingRecommendation").master("local[*]").getOrCreate();
public List<ClothingRecommendation> generatePersonalizedRecommendations(Long userId) {
Dataset<Row> userBehaviorData = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "user_behavior").load();
Dataset<Row> clothingData = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "clothing_info").load();
Dataset<Row> userProfile = userBehaviorData.filter(userBehaviorData.col("user_id").equalTo(userId));
Dataset<Row> similarUsers = userBehaviorData.groupBy("user_id")
.agg(functions.collect_list("clothing_id").as("clothing_list"))
.filter(functions.size(functions.array_intersect(
userProfile.select("clothing_list").first().getAs("clothing_list"),
functions.col("clothing_list"))).gt(3));
Dataset<Row> recommendedItems = similarUsers.join(userBehaviorData, "user_id")
.join(clothingData, "clothing_id")
.filter(functions.not(functions.col("clothing_id").isin(
userProfile.select("clothing_id").collectAsList().toArray())))
.groupBy("clothing_id", "clothing_name", "style", "color", "price")
.agg(functions.count("*").as("recommendation_score"))
.orderBy(functions.desc("recommendation_score"));
List<Row> topRecommendations = recommendedItems.limit(20).collectAsList();
List<ClothingRecommendation> recommendations = new ArrayList<>();
for (Row row : topRecommendations) {
ClothingRecommendation recommendation = new ClothingRecommendation();
recommendation.setClothingId(row.getAs("clothing_id"));
recommendation.setClothingName(row.getAs("clothing_name"));
recommendation.setStyle(row.getAs("style"));
recommendation.setColor(row.getAs("color"));
recommendation.setPrice(row.getAs("price"));
recommendation.setRecommendationScore(row.getAs("recommendation_score"));
recommendations.add(recommendation);
}
return recommendations;
}
public OutfitCombination generateOutfitCombination(Long userId, String occasion) {
Dataset<Row> userPreferences = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "user_preferences").load()
.filter(functions.col("user_id").equalTo(userId));
Dataset<Row> clothingItems = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "clothing_info").load()
.filter(functions.col("suitable_occasion").contains(occasion));
Dataset<Row> colorMatchingRules = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "color_matching_rules").load();
Dataset<Row> tops = clothingItems.filter(functions.col("category").equalTo("top"));
Dataset<Row> bottoms = clothingItems.filter(functions.col("category").equalTo("bottom"));
Dataset<Row> shoes = clothingItems.filter(functions.col("category").equalTo("shoes"));
Dataset<Row> validCombinations = tops.crossJoin(bottoms).crossJoin(shoes)
.join(colorMatchingRules,
functions.col("top_color").equalTo(functions.col("primary_color"))
.and(functions.col("bottom_color").equalTo(functions.col("secondary_color"))))
.filter(functions.col("matching_score").gt(0.7));
if (userPreferences.count() > 0) {
String preferredStyle = userPreferences.select("preferred_style").first().getString(0);
validCombinations = validCombinations.filter(
functions.col("top_style").equalTo(preferredStyle)
.or(functions.col("bottom_style").equalTo(preferredStyle)));
}
Dataset<Row> scoredCombinations = validCombinations
.withColumn("total_score",
functions.col("matching_score")
.multiply(functions.col("top_rating"))
.multiply(functions.col("bottom_rating"))
.multiply(functions.col("shoes_rating")))
.orderBy(functions.desc("total_score"));
Row bestCombination = scoredCombinations.first();
OutfitCombination outfit = new OutfitCombination();
outfit.setTopId(bestCombination.getAs("top_id"));
outfit.setBottomId(bestCombination.getAs("bottom_id"));
outfit.setShoesId(bestCombination.getAs("shoes_id"));
outfit.setTotalScore(bestCombination.getAs("total_score"));
outfit.setOccasion(occasion);
return outfit;
}
public UserStyleAnalysis analyzeUserStylePreferences(Long userId) {
Dataset<Row> purchaseHistory = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "purchase_history").load()
.filter(functions.col("user_id").equalTo(userId));
Dataset<Row> browsingHistory = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "browsing_history").load()
.filter(functions.col("user_id").equalTo(userId));
Dataset<Row> feedbackData = spark.read().format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/clothing_db")
.option("dbtable", "user_feedback").load()
.filter(functions.col("user_id").equalTo(userId));
Dataset<Row> styleAnalysis = purchaseHistory.union(browsingHistory)
.groupBy("style_category")
.agg(functions.count("*").as("frequency"),
functions.avg("rating").as("avg_rating"),
functions.sum("interaction_weight").as("total_weight"))
.withColumn("preference_score",
functions.col("frequency")
.multiply(functions.col("avg_rating"))
.multiply(functions.col("total_weight")))
.orderBy(functions.desc("preference_score"));
Dataset<Row> colorPreferences = purchaseHistory.union(browsingHistory)
.groupBy("primary_color")
.agg(functions.count("*").as("color_frequency"))
.orderBy(functions.desc("color_frequency"));
Dataset<Row> priceRangeAnalysis = purchaseHistory
.withColumn("price_range",
functions.when(functions.col("price").lt(100), "budget")
.when(functions.col("price").between(100, 500), "mid_range")
.otherwise("premium"))
.groupBy("price_range")
.agg(functions.count("*").as("purchase_count"))
.orderBy(functions.desc("purchase_count"));
UserStyleAnalysis analysis = new UserStyleAnalysis();
analysis.setUserId(userId);
analysis.setPreferredStyle(styleAnalysis.first().getAs("style_category"));
analysis.setPreferredColor(colorPreferences.first().getAs("primary_color"));
analysis.setPreferredPriceRange(priceRangeAnalysis.first().getAs("price_range"));
analysis.setStyleConfidence(styleAnalysis.first().<Double>getAs("preference_score"));
List<Row> feedbackSummary = feedbackData.groupBy("feedback_type")
.agg(functions.avg("rating").as("avg_feedback_rating")).collectAsList();
analysis.setOverallSatisfaction(feedbackSummary.stream()
.mapToDouble(row -> row.<Double>getAs("avg_feedback_rating"))
.average().orElse(0.0));
return analysis;
}
}
六、部分文档展示
七、END
💕💕文末获取源码联系计算机编程果茶熊
