django基于Spark的国漫推荐系统

**详细视频演示请联系博主
项目实现：











核心代码：
from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.sql.functions import col

class ComicRecommender:
def init(self):
self.spark = SparkSession.builder
.appName(“ComicRecommendation”)
.config(“spark.executor.memory”, “4g”)
.getOrCreate()

    # 加载数据
    self.ratings = self.spark.read.csv(
        "data/user_ratings.csv", 
        header=True, 
        inferSchema=True
    )
    self.comics = self.spark.read.csv(
        "data/comic_info.csv",
        header=True,
        inferSchema=True
    )
    
def preprocess(self):
    """数据预处理"""
    # 过滤无效评分
    self.ratings = self.ratings.filter((col("rating") >= 1) & (col("rating") <= 5))
    
    # 处理冷启动问题：添加默认评分
    popular_comics = self.ratings.groupBy("comic_id").count()
    avg_rating = self.ratings.groupBy().avg("rating").first()[0]
    
    default_ratings = popular_comics.filter(col("count") > 100) \
        .limit(100) \
        .withColumn("user_id", lit(0)) \
        .withColumn("rating", lit(avg_rating))
    
    self.ratings = self.ratings.union(default_ratings)

def train_model(self):
    """训练ALS模型"""
    # 划分训练集/测试集
    train, test = self.ratings.randomSplit([0.8, 0.2])
    
    # 配置ALS模型
    als = ALS(
        maxIter=10,
        regParam=0.01,
        userCol="user_id",
        itemCol="comic_id",
        ratingCol="rating",
        coldStartStrategy="drop"
    )
    
    # 训练模型
    self.model = als.fit(train)
    
    # 评估模型
    predictions = self.model.transform(test)
    evaluator = RegressionEvaluator(
        metricName="rmse", 
        labelCol="rating",
        predictionCol="prediction"
    )
    rmse = evaluator.evaluate(predictions)
    print(f"模型RMSE: {rmse}")
    
    return self.model

def generate_recommendations(self, user_id, num_recs=10):
    """为用户生成推荐"""
    # 创建用户DF
    user_df = self.spark.createDataFrame([(user_id,)], ["user_id"])
    
    # 获取推荐
    recs = self.model.recommendForUserSubset(user_df, num_recs)
    
    # 提取推荐结果
    rec_list = recs.select("recommendations").first()[0]
    
    # 关联漫画信息
    comic_ids = [row.comic_id for row in rec_list]
    comic_recs = self.comics.filter(col("id").isin(comic_ids))
    
    return comic_recs.toPandas().to_dict(orient="records")

def update_model(self, new_ratings):
    """增量更新模型"""
    # 将新评分添加到数据集
    new_df = self.spark.createDataFrame(new_ratings)
    self.ratings = self.ratings.union(new_df)
    
    # 重新训练模型
    self.train_model()