生成评分样例
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docs/examples/课程推荐计算评分.md
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项目地址：[https://gitea.suimu.site/lennon/recommend_system](https://gitea.suimu.site/lennon/recommend_system)
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项目中给出两个算法，一个是基于用户的协同过滤算法，一个是基于物品的协同过滤算法。
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# 数据处理
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## 原始数据集说明
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用户与课程关联数据集, 行数说明
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| 字段 | 名称 | 示例值 | 描述 | 取值范围 |
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| :---: | :---: | :---: | :---: | :---: |
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| views | 浏览记录 | 60% | 用户对课程浏览进度 | （0%，100%） |
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| favorites | 收藏记录 | 0 | 用户是否收藏课程，1 为收藏 | enum(0,1) |
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| likes | 点赞记录 | 1 | 用户是否点赞课程，1 为点赞 | enum(0,1) |
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| comments | 评论记录 | ["Loved it", "Would buy again"] | 用户对课程的评论，为字符串数组 | ["Great product!", "Loved it", "Would buy again"] |
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| shares | 分享记录 | 1 | 用户是否分享课程，1 为分享 | enum(0,1) |
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| feedbacks | 反馈记录 | [ "Shipping was fast"] | 用户对课程的反馈，为字符串数组 | ["The product was good", "Shipping was fast"] |
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| ratings | 评分记录 | 3 | 用户对课程的评分 | （1，5） |
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## 中间状态，文本情感计算
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处理文本情感之后的数据，示例如下：
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| **用户编码** | **课程编码** | **浏览记录** | **收藏记录** | **点赞记录** | **评论记录** | **分享记录** | **反馈记录** | **评分记录** |
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| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
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| 1 | 1 | 0.28 | 1 | 0 | 0.25 | 1 | 0.87 | 1 |
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| 1 | 2 | 0.49 | 0 | 1 | 0.76 | 0 | 0.65 | 3 |
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其中，评论记录和反馈记录通过 NLP 的情感分析，得到 （0,1）之间的两位小数。偏向 1 表示正向情感。
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```python
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pip install snownlp
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```
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```python
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from snownlp import SnowNLP
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text1 = "这个产品真的很好用！"
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s = SnowNLP(text1)
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print(s.sentiments) # 输出情感得分 0.8380894562907347
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from snownlp import SnowNLP
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text = "好烦啊，和参数对不上！"
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s = SnowNLP(text)
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print(s.sentiments) # 输出情感得分 0.2734196629160368
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```
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## 计算 User-Item 的评分
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在原始 ml-100k 数据集中，用户和电影之间的评分是手动打的，本项目中的实现逻辑也比较简单。计算物品或者用户的邻居逻辑都在类 `CoreMath`中。所以这边需要根据 浏览记录、收藏记录、点赞记录、评论记录、反馈记录、评分记录等信息计算出一个评分。
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实现逻辑是将所有的信息都标准化为一个 0 到 1 之间数，然后按照不同信息的重要程度给一个权重。计算出一个 1 到 5 之间的分数。这样就不需要改动原有算法的代码了。
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### 权重分配说明：
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+ **浏览记录 (views)**: 虽然浏览行为重要，但它属于较为被动的行为。建议赋予较低的权重。
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+ **收藏记录 (favorites)**: 收藏表明用户对产品有一定的兴趣，建议赋予中等权重。
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+ **点赞记录 (likes)**: 点赞表示用户的积极反馈，建议赋予中等偏高的权重。
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+ **评论记录 (comments)**: 评论能直接反映用户的想法，建议赋予较高的权重。
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+ **分享记录 (shares)**: 分享表明用户愿意向他人推荐产品，建议赋予中等偏高的权重。
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+ **反馈记录 (feedbacks)**: 反馈通常比评论更详细，建议赋予较高的权重。
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+ **评分记录 (ratings)**: 评分是最直接的用户评分，建议赋予最高的权重。
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### 权重分配建议
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```python
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weights = {
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'views': 0.05, # 浏览记录：较低权重
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'favorites': 0.1, # 收藏记录：中等权重
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'likes': 0.15, # 点赞记录：中等偏高权重
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'comments': 0.2, # 评论记录：较高权重
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'shares': 0.15, # 分享记录：中等偏高权重
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'feedbacks': 0.2, # 反馈记录：较高权重
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'ratings': 0.15 # 评分记录：最高权重
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}
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```
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### 代码示例
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```python
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from typing import Dict
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from snownlp import SnowNLP
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def calculate_composite_score(
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views: float,
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favorites: int,
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likes: int,
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comments: list[str],
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shares: int,
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feedbacks: list[str],
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rating: int,
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weights: Dict[str, float] = None
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) -> float:
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if weights is None:
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print("No weights provided, using default values.")
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weights = {
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'views': 0.01,
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'favorites': 0.1,
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'likes': 0.125,
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'comments': 0.175,
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'shares': 0.125,
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'feedbacks': 0.175,
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'rating': 0.29
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}
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print(f"Weights: {weights}")
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# 量化 comments 和 feedback 数据，如果列表为空则默认为0
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avg_comment_score = np.mean([SnowNLP(comment).sentiments for comment in comments]) if comments else 0
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avg_feedback_score = np.mean([SnowNLP(feedback).sentiments for feedback in feedbacks]) if feedbacks else 0
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# 格式化为两位小数
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avg_comment_score_formatted = round(avg_comment_score, 2)
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avg_feedback_score_formatted = round(avg_feedback_score, 2)
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print(f"Average comment score: {avg_comment_score_formatted}")
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print(f"Average feedback length: {avg_feedback_score_formatted}")
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# 将评分数据缩放到 0-1
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scale_rating = rating * 0.2
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# Calculate the weighted score
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score = (
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views * weights['views'] +
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favorites * weights['favorites'] +
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likes * weights['likes'] +
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avg_comment_score_formatted * weights['comments'] +
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shares * weights['shares'] +
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avg_feedback_score_formatted * weights['feedbacks'] +
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scale_rating * weights['rating']
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)
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print(f"Score: {score}")
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# Ensure the score is in the range [1, 5]
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score = max(1, min(5, score * 5))
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return round(score, 2)
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# 示例用法
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views = 75 * 0.01 # 假设 75% 的用户浏览了这个 item
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favorites = 1
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likes = 0
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comments = ["非常棒的产品!", "超爱的", "下次还买"]
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shares = 1
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feedbacks = ["产品很好", "发货速度很快"]
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rating = 5
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composite_score = calculate_composite_score(views, favorites, likes, comments, shares, feedbacks, rating)
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print("Composite Score:", composite_score)
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```
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