pyspark推荐系统

PySpark中的推荐系统模块是pyspark.ml.recommendation。这个模块提供了使用ALS（交替最小二乘）算法的推荐系统功能。你可以在官方文档中找到更多关于这个模块的信息，链接是：api/python/pyspark.ml.html#module-pyspark.ml.recommendation。\[1\]

要在终端中使用PySpark的推荐系统模块，你可以按照以下步骤准备数据：
1. 进入到你的Python工作目录，可以使用命令：cd ~/pythonwork/ipynotebook。
2. 输入以下命令来启动PySpark并设置相关参数：
PYSPARK_DRIVER_PYTHON=ipython PYSPARK_DRIVER_PYTHON_OPTS="notebook" HADOOP_CONF_IR=/usr/local/hadoop/etc/hadoop MASTER=yarn-client pyspark。
3. 准备好你的数据。\[2\]

如果你想在PySpark中使用推荐系统模块进行筛选操作，你可以使用filter函数来筛选出包含特定关键词的行。下面是一个示例代码：

lines = sc.parallelize(\['Spark is very fast', 'My name is Li Lei'\])
# 筛选出含有“Spark”的行，操作为并行
linesWithSpark = lines.filter(lambda line: "Spark" in line)
# 每行并行打印
linesWithSpark.foreach(print)
# 输出结果：Spark is very fast

这段代码会筛选出包含"Spark"关键词的行，并将结果打印出来。\[3\]
#### 引用[.reference_title]
- *1* [PySpark-推荐系统-RecommenderSystem](https://blog.csdn.net/geek6/article/details/104274739)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [基于Python Spark的推荐系统](https://blog.csdn.net/weixin_40170902/article/details/82585607)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *3* [PySpark基本入门（附python代码示例）](https://blog.csdn.net/weixin_54707168/article/details/122757247)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
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