推荐系统：电商推荐系统架构

灰太狼 2023-10-05 17:42 72阅读 0赞

## 一、数据 ##

### 1、数据生命周期 ###

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### 2、数据源解析 ###

#### 2.1 商品信息 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 1]

#### 2.2 用户评分数据 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 2]

#### 2.3 主要数据模型 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 3]

### 3、大数据处理流程 ###

#### 3.1 大数据离线处理流程 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 4]

#### 3.2 大数据实时处理流程 ####

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 5]

## 二、大数据推荐项目系统架构 ##

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70]

### 1、项目系统架构 ###

![在这里插入图片描述][62cdfe57bb384b558614e4c5b8915673.png_pic_center]

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 6]  
![在这里插入图片描述][13ddfbcd575c456ba00fcdfb5fc3f08f.png_pic_center]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 7]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70 2]

### 1、离线统计模块 ###

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 8]

#### 1.1 历史热门商品统计 ####

*  统计所有历史数据中每个商品的评分数，得：RateMoreProducts 数据结构：productId，count
    
        select productId, count(productId) as count from ratings group by productId order by count desc

#### 1.2 近期热门商品统计 ####

*  统计每月的商品评分个数，就代表了商品近期的热门度，得：ratingOfMonth、RateMoreRecentlyProducts
    
        select productId, score, changeDate(timestamp) as yearmonth from ratings
    
        select productId, count(productId) as count ,yearmonth from ratingOfMonth group by yearmonth, productId order by yearmonth desc,count desc
    
    changDate ：UDF函数，使用 SimpleDateFormat 对 Date 进行格式转化，转化格式为“yyyyMM”  
    RateMoreRecentlyProducts 数据结构：productId，count，yearmonth

#### 1.3 商品平均评分统计 ####

*  AverageProducts 数据结构：productId，avg
    
        select productId, avg(score)

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