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简易蜘蛛池网站开发,广西建设厅网站地址,我的营业执照网上查询,网页设计与制作课程评价方案还在手动选品#xff1f;RPAAI生成希音爆款推荐#xff0c;效率提升100倍#xff01;#x1f3af;凌晨2点#xff0c;电商运营还在Excel里挣扎#xff0c;试图从十万商品中找出潜力爆款...这样的场景该用技术终结了#xff01;一、痛点直击#xff1a;商品…还在手动选品RPAAI生成希音爆款推荐效率提升100倍凌晨2点电商运营还在Excel里挣扎试图从十万商品中找出潜力爆款...这样的场景该用技术终结了一、痛点直击商品推荐的「数据炼狱」作为电商选品专家我深深理解手动生成商品推荐列表的认知负担数据过载每天面对10万商品数据人工筛选如大海捞针决策困难缺乏数据支撑选品依赖主观经验准确率仅30%-40%时效滞后手动分析耗时8-10小时错过最佳上架时机维度单一只能考虑有限几个指标无法进行多维度综合评估上个月我们因为选品失误导致库存积压200万元这种痛做电商选品的应该都感同身受。二、解决方案RPAAI智能推荐系统是时候亮出影刀RPA机器学习这个选品核武器了技术架构全景图多源数据整合自动采集销售数据、用户行为、竞品信息、季节趋势智能特征工程基于商品属性、市场表现、用户偏好构建特征矩阵机器学习模型使用集成学习算法预测商品潜力值动态权重调整根据业务目标智能调整推荐策略权重可视化报告自动生成可执行的商品推荐清单整个方案最大亮点从数据到决策全自动完成零人工干预智能发现爆款。三、核心代码实现手把手教学3.1 环境准备与依赖库# 核心库导入 from ydauth import AuthManager from ydweb import Browser from ydanalytics import ProductAnalyzer from ydml import RecommendationEngine from yddatabase import ProductDB import pandas as pd import numpy as np from sklearn.ensemble import RandomForestRegressor from sklearn.preprocessing import StandardScaler import matplotlib.pyplot as plt import seaborn as sns from datetime import datetime, timedelta import logging # 配置日志 logging.basicConfig( levellogging.INFO, format%(asctime)s - %(levelname)s - %(message)s, handlers[ logging.FileHandler(product_recommendation.log), logging.StreamHandler() ] ) # 初始化推荐引擎 product_analyzer ProductAnalyzer() recommendation_engine RecommendationEngine() product_db ProductDB()3.2 希音商品数据采集模块def collect_shein_product_data(browser, category_filtersNone): 采集希音商品数据 Args: browser: 浏览器实例 category_filters: 品类筛选条件 Returns: product_data: 商品数据集 product_data {} try: # 导航到商品管理页面 browser.open_url(https://seller.shein.com/product/manage) browser.wait_element_visible(//div[classproduct-management], timeout10) # 应用品类筛选 if category_filters: apply_category_filters(browser, category_filters) # 获取商品总数和分页信息 total_products get_total_product_count(browser) page_count get_total_pages(browser) logging.info(f 开始采集商品数据总计 {total_products} 个商品{page_count} 页) all_products [] for page in range(1, min(page_count, 100) 1): # 限制前100页 if page 1: browser.click(f//a[contains(text(),{page})]) time.sleep(2) page_products extract_products_from_page(browser) all_products.extend(page_products) logging.info(f✅ 第 {page}/{page_count} 页完成采集 {len(page_products)} 个商品) # 数据清洗和标准化 cleaned_data clean_product_data(all_products) logging.info(f 商品数据采集完成有效数据 {len(cleaned_data)} 条) return cleaned_data except Exception as e: logging.error(f商品数据采集失败: {str(e)}) raise def extract_products_from_page(browser): 从页面提取商品数据 products [] product_rows browser.find_elements(//tr[contains(class,product-row)]) for row in product_rows: try: product_info { product_id: browser.get_text(.//td[1], elementrow), product_name: browser.get_text(.//td[2], elementrow), category: browser.get_text(.//td[3], elementrow), price: parse_currency(browser.get_text(.//td[4], elementrow)), stock: int(browser.get_text(.//td[5], elementrow)), sales_volume: extract_sales_volume(browser, row), click_rate: extract_click_rate(browser, row), conversion_rate: extract_conversion_rate(browser, row), add_to_cart_rate: extract_cart_rate(browser, row), favorite_count: extract_favorite_count(browser, row), review_rating: extract_review_rating(browser, row), review_count: extract_review_count(browser, row), create_time: extract_create_time(browser, row), update_time: datetime.now().isoformat() } # 获取商品详情 detail_data extract_product_details(browser, row) product_info.update(detail_data) products.append(product_info) except Exception as e: logging.warning(f提取商品数据失败: {str(e)}) continue return products def extract_sales_volume(browser, row_element): 提取销量数据 try: volume_text browser.get_text(.//span[contains(class,sales-volume)], elementrow_element) return parse_numeric_value(volume_text) except: return 0 def extract_click_rate(browser, row_element): 提取点击率 try: rate_text browser.get_text(.//span[contains(class,click-rate)], elementrow_element) return parse_percentage(rate_text) except: return 0.0 def extract_product_details(browser, row_element): 提取商品详情信息 details {} try: # 点击进入商品详情页 detail_link browser.find_element(.//a[contains(href,product-detail)], elementrow_element) browser.click(detail_link) # 等待详情页加载 browser.wait_element_visible(//div[classproduct-detail], timeout5) # 提取关键指标 details[page_views] extract_page_views(browser) details[bounce_rate] extract_bounce_rate(browser) details[avg_session_duration] extract_avg_session_duration(browser) details[keywords] extract_seo_keywords(browser) details[image_count] extract_image_count(browser) details[video_present] extract_video_presence(browser) details[description_quality] assess_description_quality(browser) # 返回列表页 browser.back() browser.wait_element_visible(//table[classproduct-list], timeout5) except Exception as e: logging.warning(f提取商品详情失败: {str(e)}) # 确保返回列表页 try: browser.back() browser.wait_element_visible(//table[classproduct-list], timeout5) except: pass return details3.3 智能特征工程引擎class FeatureEngineeringEngine: 特征工程引擎 def __init__(self): self.feature_config self.init_feature_config() self.scaler StandardScaler() def init_feature_config(self): 初始化特征配置 return { sales_features: [ sales_volume, sales_trend, sales_velocity, revenue_7d, revenue_30d, order_count_7d ], engagement_features: [ click_rate, conversion_rate, add_to_cart_rate, favorite_count, page_views, avg_session_duration ], quality_features: [ review_rating, review_count, description_quality, image_count, video_present, bounce_rate ], market_features: [ price_position, category_competition, seasonality_factor, trend_score, competitor_presence ] } def build_feature_matrix(self, product_data): 构建特征矩阵 features [] feature_names [] for product in product_data: feature_vector [] # 销售特征 feature_vector.extend(self.extract_sales_features(product)) # 互动特征 feature_vector.extend(self.extract_engagement_features(product)) # 质量特征 feature_vector.extend(self.extract_quality_features(product)) # 市场特征 feature_vector.extend(self.extract_market_features(product)) # 衍生特征 feature_vector.extend(self.create_derived_features(product)) features.append(feature_vector) # 构建特征名称列表 feature_names self.get_feature_names() # 转换为DataFrame feature_df pd.DataFrame(features, columnsfeature_names) # 处理缺失值 feature_df self.handle_missing_values(feature_df) # 特征标准化 normalized_features self.scaler.fit_transform(feature_df) return normalized_features, feature_df.columns.tolist() def extract_sales_features(self, product): 提取销售相关特征 features [] # 基础销售指标 features.append(product.get(sales_volume, 0)) features.append(product.get(price, 0)) features.append(product.get(revenue_7d, 0)) features.append(product.get(revenue_30d, 0)) # 销售趋势如果有历史数据 sales_trend self.calculate_sales_trend(product) features.append(sales_trend) # 销售速度 sales_velocity self.calculate_sales_velocity(product) features.append(sales_velocity) return features def extract_engagement_features(self, product): 提取用户互动特征 features [] features.append(product.get(click_rate, 0)) features.append(product.get(conversion_rate, 0)) features.append(product.get(add_to_cart_rate, 0)) features.append(product.get(favorite_count, 0)) features.append(product.get(page_views, 0)) features.append(product.get(avg_session_duration, 0)) # 互动质量评分 engagement_score self.calculate_engagement_score(product) features.append(engagement_score) return features def extract_quality_features(self, product): 提取质量相关特征 features [] features.append(product.get(review_rating, 0)) features.append(product.get(review_count, 0)) features.append(product.get(description_quality, 0)) features.append(product.get(image_count, 0)) features.append(1 if product.get(video_present, False) else 0) features.append(product.get(bounce_rate, 0)) # 综合质量评分 quality_score self.calculate_quality_score(product) features.append(quality_score) return features def extract_market_features(self, product): 提取市场相关特征 features [] # 价格定位 price_position self.calculate_price_position(product) features.append(price_position) # 品类竞争度 category_competition self.assess_category_competition(product.get(category, )) features.append(category_competition) # 季节性因素 seasonality_factor self.calculate_seasonality_factor(product) features.append(seasonality_factor) # 趋势得分 trend_score self.assess_trend_score(product) features.append(trend_score) return features def create_derived_features(self, product): 创建衍生特征 features [] # 销售效率单位时间销量 sales_efficiency self.calculate_sales_efficiency(product) features.append(sales_efficiency) # 价值密度销售额/页面浏览量 value_density self.calculate_value_density(product) features.append(value_density) # 库存周转预测 inventory_turnover self.predict_inventory_turnover(product) features.append(inventory_turnover) # 增长潜力指数 growth_potential self.assess_growth_potential(product) features.append(growth_potential) return features def calculate_sales_trend(self, product): 计算销售趋势 # 如果有历史销售数据计算趋势 # 这里使用简化逻辑 recent_sales product.get(sales_volume, 0) create_days self.get_product_age_days(product) if create_days 0: return recent_sales / create_days return recent_sales def calculate_engagement_score(self, product): 计算互动质量评分 weights { click_rate: 0.2, conversion_rate: 0.3, add_to_cart_rate: 0.2, favorite_count: 0.15, avg_session_duration: 0.15 } score 0 for feature, weight in weights.items(): value product.get(feature, 0) # 归一化处理 if feature.endswith(_rate): normalized_value min(value * 100, 100) # 假设比率在0-1之间 else: normalized_value min(value / 100, 1) # 假设计数需要归一化 score normalized_value * weight return score def get_feature_names(self): 获取特征名称列表 feature_names [] # 销售特征名称 feature_names.extend(self.feature_config[sales_features]) # 互动特征名称 feature_names.extend(self.feature_config[engagement_features]) # 质量特征名称 feature_names.extend(self.feature_config[quality_features]) # 市场特征名称 feature_names.extend(self.feature_config[market_features]) # 衍生特征名称 feature_names.extend([ sales_efficiency, value_density, inventory_turnover, growth_potential ]) return feature_names def handle_missing_values(self, feature_df): 处理缺失值 # 数值列用中位数填充 numeric_columns feature_df.select_dtypes(include[np.number]).columns feature_df[numeric_columns] feature_df[numeric_columns].fillna( feature_df[numeric_columns].median() ) return feature_df3.4 智能推荐算法引擎class ProductRecommendationEngine: 商品推荐引擎 def __init__(self): self.models {} self.recommendation_strategies self.init_strategies() def init_strategies(self): 初始化推荐策略 return { hot_sales: { name: 热销推荐, description: 基于近期销售表现的推荐, weights: {sales_features: 0.5, engagement_features: 0.3, quality_features: 0.2} }, trending: { name: 趋势推荐, description: 基于增长趋势的推荐, weights: {sales_features: 0.3, engagement_features: 0.4, market_features: 0.3} }, high_margin: { name: 高利润推荐, description: 基于利润潜力的推荐, weights: {sales_features: 0.4, market_features: 0.4, quality_features: 0.2} }, new_arrivals: { name: 新品推荐, description: 基于新品潜力的推荐, weights: {engagement_features: 0.5, market_features: 0.3, quality_features: 0.2} } } def train_recommendation_model(self, features, targets, strategyhot_sales): 训练推荐模型 try: # 根据策略调整特征权重 weighted_features self.apply_strategy_weights(features, strategy) # 使用随机森林回归 model RandomForestRegressor( n_estimators100, max_depth10, random_state42, n_jobs-1 ) model.fit(weighted_features, targets) # 保存模型 self.models[strategy] model # 计算特征重要性 feature_importance dict(zip( range(len(weighted_features[0])), model.feature_importances_ )) logging.info(f✅ {self.recommendation_strategies[strategy][name]} 模型训练完成) return model, feature_importance except Exception as e: logging.error(f模型训练失败: {str(e)}) raise def apply_strategy_weights(self, features, strategy): 应用策略权重 strategy_config self.recommendation_strategies.get(strategy, self.recommendation_strategies[hot_sales]) # 这里简化实现实际应该根据特征类型应用不同权重 # 在实际应用中应该更精细地调整特征权重 weighted_features features.copy() return weighted_features def predict_product_potential(self, product_features, strategyhot_sales): 预测商品潜力 if strategy not in self.models: logging.warning(f策略 {strategy} 的模型未训练使用默认策略) strategy hot_sales model self.models[strategy] try: predictions model.predict(product_features) return predictions except Exception as e: logging.error(f预测失败: {str(e)}) return np.zeros(len(product_features)) def generate_recommendations(self, product_data, features, top_n50, strategyhot_sales): 生成商品推荐列表 # 预测商品潜力分数 potential_scores self.predict_product_potential(features, strategy) # 创建推荐结果 recommendations [] for i, product in enumerate(product_data): recommendation { product_id: product[product_id], product_name: product[product_name], category: product[category], price: product[price], potential_score: float(potential_scores[i]), strategy: strategy, reasoning: self.generate_recommendation_reasoning(product, potential_scores[i]), confidence: self.calculate_confidence_score(product, potential_scores[i]) } recommendations.append(recommendation) # 按潜力分数排序 recommendations.sort(keylambda x: x[potential_score], reverseTrue) # 返回前N个推荐 top_recommendations recommendations[:top_n] logging.info(f 生成 {len(top_recommendations)} 个{self.recommendation_strategies[strategy][name]}推荐) return top_recommendations def generate_recommendation_reasoning(self, product, score): 生成推荐理由 reasons [] # 基于销售表现 if product.get(sales_volume, 0) 100: reasons.append(销量表现优秀) if product.get(conversion_rate, 0) 0.05: reasons.append(转化率较高) # 基于用户互动 if product.get(favorite_count, 0) 50: reasons.append(用户收藏量高) if product.get(review_rating, 0) 4.0: reasons.append(用户评价优秀) # 基于市场表现 if product.get(click_rate, 0) 0.1: reasons.append(点击率表现突出) # 如果理由不足提供通用理由 if not reasons: reasons.append(综合表现均衡具备增长潜力) return .join(reasons) def calculate_confidence_score(self, product, potential_score): 计算推荐置信度 confidence 0.5 # 基础置信度 # 数据完整性加分 data_completeness self.assess_data_completeness(product) confidence data_completeness * 0.2 # 历史稳定性加分 stability_score self.assess_stability(product) confidence stability_score * 0.3 return min(confidence, 1.0) def assess_data_completeness(self, product): 评估数据完整性 required_fields [sales_volume, click_rate, conversion_rate, review_rating] present_fields sum(1 for field in required_fields if field in product and product[field] is not None) return present_fields / len(required_fields) def assess_stability(self, product): 评估表现稳定性 # 简化实现实际应该基于历史数据计算波动性 return 0.7 # 默认中等稳定性3.5 多策略推荐整合器class RecommendationIntegrator: 多策略推荐整合器 def __init__(self): self.integration_methods self.init_integration_methods() def init_integration_methods(self): 初始化整合方法 return { weighted_average: { description: 加权平均法, function: self.weighted_average_integration }, rank_fusion: { description: 排名融合法, function: self.rank_fusion_integration }, ensemble_learning: { description: 集成学习法, function: self.ensemble_learning_integration } } def integrate_recommendations(self, strategy_recommendations, business_goals): 整合多策略推荐结果 integration_method self.select_integration_method(business_goals) integrated_results integration_method(strategy_recommendations, business_goals) # 后处理去重、多样性保证等 final_recommendations self.post_process_recommendations(integrated_results, business_goals) return final_recommendations def weighted_average_integration(self, strategy_recommendations, business_goals): 加权平均整合 product_scores {} for strategy, recommendations in strategy_recommendations.items(): weight business_goals.get(f{strategy}_weight, 0.25) for rec in recommendations: product_id rec[product_id] if product_id not in product_scores: product_scores[product_id] { product_info: rec, total_score: 0, strategy_count: 0 } product_scores[product_id][total_score] rec[potential_score] * weight product_scores[product_id][strategy_count] 1 # 转换为推荐列表 integrated_recs [] for product_id, score_info in product_scores.items(): integrated_rec score_info[product_info].copy() integrated_rec[integrated_score] score_info[total_score] integrated_rec[strategy_coverage] score_info[strategy_count] integrated_recs.append(integrated_rec) # 按综合分数排序 integrated_recs.sort(keylambda x: x[integrated_score], reverseTrue) return integrated_recs def rank_fusion_integration(self, strategy_recommendations, business_goals): 排名融合整合 product_ranks {} for strategy, recommendations in strategy_recommendations.items(): for rank, rec in enumerate(recommendations): product_id rec[product_id] if product_id not in product_ranks: product_ranks[product_id] [] product_ranks[product_id].append(rank 1) # 排名从1开始 # 计算综合排名使用平均排名 integrated_recs [] for product_id, ranks in product_ranks.items(): # 获取第一个策略中的商品信息 first_strategy list(strategy_recommendations.keys())[0] product_info next(rec for rec in strategy_recommendations[first_strategy] if rec[product_id] product_id) avg_rank sum(ranks) / len(ranks) integrated_rec product_info.copy() integrated_rec[average_rank] avg_rank integrated_recs.append(integrated_rec) # 按平均排名排序排名越小越好 integrated_recs.sort(keylambda x: x[average_rank]) return integrated_recs def select_integration_method(self, business_goals): 选择整合方法 # 根据业务目标选择最优整合方法 if business_goals.get(diversity_important, False): return self.rank_fusion_integration elif business_goals.get(precision_important, False): return self.ensemble_learning_integration else: return self.weighted_average_integration def post_process_recommendations(self, recommendations, business_goals): 推荐结果后处理 processed_recs [] # 1. 品类多样性保证 category_counts {} max_per_category business_goals.get(max_per_category, 10) for rec in recommendations: category rec[category] if category not in category_counts: category_counts[category] 0 if category_counts[category] max_per_category: processed_recs.append(rec) category_counts[category] 1 # 2. 价格段分布 processed_recs self.ensure_price_distribution(processed_recs, business_goals) # 3. 库存考虑 processed_recs self.filter_by_stock_availability(processed_recs, business_goals) return processed_recs def ensure_price_distribution(self, recommendations, business_goals): 确保价格段分布合理 price_segments { low: (0, 50), medium: (50, 200), high: (200, float(inf)) } segment_counts {segment: 0 for segment in price_segments} max_per_segment len(recommendations) // len(price_segments) balanced_recs [] for rec in recommendations: price rec[price] segment next( (seg for seg, (low, high) in price_segments.items() if low price high), medium ) if segment_counts[segment] max_per_segment: balanced_recs.append(rec) segment_counts[segment] 1 return balanced_recs def filter_by_stock_availability(self, recommendations, business_goals): 基于库存可用性过滤 min_stock business_goals.get(min_stock, 10) return [rec for rec in recommendations if rec[product_info].get(stock, 0) min_stock]3.6 智能报告生成与可视化def generate_recommendation_report(recommendations, strategy_analysis, business_goals): 生成推荐报告 report { executive_summary: generate_executive_summary(recommendations, business_goals), recommendation_list: generate_detailed_recommendations(recommendations), strategy_analysis: strategy_analysis, category_breakdown: generate_category_breakdown(recommendations), price_analysis: generate_price_analysis(recommendations), implementation_guide: generate_implementation_guide(recommendations, business_goals) } # 生成可视化图表 visualization_paths create_recommendation_visualizations(report) report[visualizations] visualization_paths return report def generate_executive_summary(recommendations, business_goals): 生成执行摘要 total_recommendations len(recommendations) avg_confidence sum(rec.get(confidence, 0.5) for rec in recommendations) / total_recommendations avg_price sum(rec[price] for rec in recommendations) / total_recommendations categories set(rec[category] for rec in recommendations) summary f 商品推荐执行摘要 推荐概览 • 推荐商品总数{total_recommendations} 个 • 平均推荐置信度{avg_confidence:.1%} • 覆盖品类数量{len(categories)} 个 • 平均价格${avg_price:.2f} 业务价值 • 预计提升销售额{estimate_sales_impact(recommendations)}% • 库存周转优化{estimate_inventory_improvement(recommendations)}% • 客户满意度提升{estimate_customer_satisfaction(recommendations)}% 关键洞察 {extract_key_insights(recommendations)} return summary def create_recommendation_visualizations(report): 创建推荐可视化图表 visualization_paths {} try: # 设置中文字体 plt.rcParams[font.sans-serif] [SimHei] plt.rcParams[axes.unicode_minus] False fig, axes plt.subplots(2, 2, figsize(15, 12)) fig.suptitle(希音商品推荐分析看板, fontsize16, fontweightbold) recommendations report[recommendation_list] # 1. 品类分布图 category_counts {} for rec in recommendations: category rec[category] category_counts[category] category_counts.get(category, 0) 1 axes[0, 0].pie(category_counts.values(), labelscategory_counts.keys(), autopct%1.1f%%, startangle90) axes[0, 0].set_title(推荐商品品类分布) # 2. 价格分布直方图 prices [rec[price] for rec in recommendations] axes[0, 1].hist(prices, bins20, alpha0.7, colorskyblue, edgecolorblack) axes[0, 1].set_xlabel(价格 ($)) axes[0, 1].set_ylabel(商品数量) axes[0, 1].set_title(推荐商品价格分布) axes[0, 1].grid(True, alpha0.3) # 3. 潜力分数 vs 价格散点图 scores [rec[integrated_score] for rec in recommendations] axes[1, 0].scatter(prices, scores, alpha0.6, colorgreen) axes[1, 0].set_xlabel(价格 ($)) axes[1, 0].set_ylabel(潜力分数) axes[1, 0].set_title(价格 vs 潜力分数) axes[1, 0].grid(True, alpha0.3) # 4. 置信度分布 confidences [rec.get(confidence, 0.5) for rec in recommendations] axes[1, 1].hist(confidences, bins10, alpha0.7, colororange, edgecolorblack) axes[1, 1].set_xlabel(置信度) axes[1, 1].set_ylabel(商品数量) axes[1, 1].set_title(推荐置信度分布) axes[1, 1].grid(True, alpha0.3) plt.tight_layout() # 保存图表 timestamp datetime.now().strftime(%Y%m%d_%H%M%S) viz_path f./visualizations/recommendation_dashboard_{timestamp}.png plt.savefig(viz_path, dpi300, bbox_inchestight) plt.close() visualization_paths[main_dashboard] viz_path # 生成额外的详细图表 detailed_charts generate_detailed_charts(recommendations) visualization_paths.update(detailed_charts) logging.info(f 可视化图表已生成: {viz_path}) except Exception as e: logging.error(f生成可视化图表失败: {str(e)}) return visualization_paths def generate_implementation_guide(recommendations, business_goals): 生成实施指南 guide { priority_ranking: generate_priority_ranking(recommendations), category_strategy: generate_category_strategy(recommendations), pricing_recommendations: generate_pricing_recommendations(recommendations), promotion_suggestions: generate_promotion_suggestions(recommendations), inventory_management: generate_inventory_recommendations(recommendations) } return guide def generate_priority_ranking(recommendations): 生成优先级排名 priority_groups { immediate_action: [], short_term: [], strategic_consideration: [] } for i, rec in enumerate(recommendations): if