写基于Spark SQL的房屋推荐算法的具体代码

好的，以下是一份基于Spark SQL的房屋推荐算法的Python代码示例：

# 导入相关库
from pyspark.sql.functions import col, explode, split
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.recommendation import ALS

# 加载房屋数据和用户行为数据
house_data = spark.read.csv("house_data.csv", header=True, inferSchema=True)
user_behavior = spark.read.csv("user_behavior.csv", header=True, inferSchema=True)

# 对用户行为数据进行特征工程
user_behavior = user_behavior.withColumn("view_time_bins", 
                                           when(col("view_time") < 60, "0-1min")
                                           .when(col("view_time") < 180, "1-3min")
                                           .when(col("view_time") < 300, "3-5min")
                                           .otherwise("5min+"))
user_behavior = user_behavior.withColumn("price_normalized", 
                                           (col("price") - 100000) / 500000)

# 将用户行为数据和房屋数据进行关联，生成大表
joined_data = user_behavior.join(house_data, on=["house_id"], how="inner")

# 特征向量化
assembler = VectorAssembler(inputCols=["view_time_bins", "price_normalized", "house_type", "house_area"], 
                            outputCol="features")
joined_data = assembler.transform(joined_data)

# 拆分训练集和测试集
(train_data, test_data) = joined_data.randomSplit([0.8, 0.2])

# 建立ALS模型
als = ALS(rank=10, maxIter=10, regParam=0.01, userCol="user_id", itemCol="house_id", ratingCol="rating")
model = als.fit(train_data)

# 对测试集进行预测
predictions = model.transform(test_data)

# 选取每个用户的前N个推荐房屋
user_recommendations = predictions.groupBy("user_id").agg(collect_list("house_id").alias("recommended_houses"))
user_recommendations = user_recommendations.withColumn("top_recommendations", 
                                                       explode(split(col("recommended_houses"), ", ")))
user_recommendations = user_recommendations.select("user_id", "top_recommendations")

# 展示推荐结果
user_recommendations.show()

请根据具体业务需求进行调整和优化。