基于spark用户推荐代码 除了ALS方法
时间: 2023-08-01 21:07:24 浏览: 103
基于Spark MLlib ALS的音乐推荐系统.zip
除了ALS方法,Spark还提供了其他的用户推荐算法,包括:
1. 基于内容的推荐(Content-Based Recommendation):这种推荐方法根据用户喜欢的物品的属性,找到与其相似的物品进行推荐。在Spark中,可以使用MLlib库中的TF-IDF算法和余弦相似度计算来实现基于内容的推荐。
2. 协同过滤推荐(Collaborative Filtering Recommendation):这种推荐方法使用用户和物品之间的交互信息(如评分、点击等)来推荐物品。在Spark中,除了ALS方法以外,还提供了基于矩阵分解的推荐算法(如SVD++),以及基于模型的协同过滤算法(如基于隐语义模型的推荐算法)。
3. 混合推荐(Hybrid Recommendation):这种推荐方法综合了多种推荐算法,例如基于内容的推荐和协同过滤推荐,以提高推荐的准确度和覆盖率。在Spark中,可以通过将多个推荐算法的结果进行加权求和来实现混合推荐。
下面是基于内容的推荐代码示例:
```python
from pyspark.ml.feature import HashingTF, IDF, Tokenizer
from pyspark.ml.linalg import Vectors
from pyspark.sql.functions import col, udf
from pyspark.sql.types import IntegerType
# 创建电影数据集
movies = spark.createDataFrame([
(0, "The Shawshank Redemption", "drama"),
(1, "The Godfather", "drama"),
(2, "The Dark Knight", "action"),
(3, "The Lord of the Rings: The Fellowship of the Ring", "adventure"),
(4, "The Matrix", "action"),
(5, "Inception", "action"),
(6, "Forrest Gump", "drama"),
(7, "The Lord of the Rings: The Return of the King", "adventure"),
(8, "The Godfather: Part II", "drama"),
(9, "The Lord of the Rings: The Two Towers", "adventure")
], ["movieId", "title", "genre"])
# 创建用户评分数据集
ratings = spark.createDataFrame([
(0, 0, 5),
(0, 1, 4),
(0, 2, 3),
(0, 3, 5),
(0, 4, 4),
(0, 5, 3),
(1, 0, 4),
(1, 1, 5),
(1, 2, 4),
(1, 3, 3),
(1, 4, 4),
(1, 5, 5),
(2, 0, 3),
(2, 1, 4),
(2, 3, 5),
(2, 4, 3),
(2, 5, 4),
(3, 1, 5),
(3, 3, 4),
(3, 4, 5),
(3, 5, 5),
(4, 0, 4),
(4, 1, 3),
(4, 2, 5),
(4, 3, 4),
(4, 4, 3),
(4, 5, 4)
], ["userId", "movieId", "rating"])
# 将电影数据集转换为特征向量
tokenizer = Tokenizer(inputCol="genre", outputCol="words")
wordsData = tokenizer.transform(movies)
hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
featurizedData = hashingTF.transform(wordsData)
idf = IDF(inputCol="rawFeatures", outputCol="features")
idfModel = idf.fit(featurizedData)
rescaledData = idfModel.transform(featurizedData)
# 定义用户喜好函数,用于计算用户喜欢的电影类型
def userLikes(userId):
userMovies = ratings.filter(col("userId") == userId).select("movieId")
userGenres = movies.join(userMovies, "movieId").select("genre")
genres = [row.genre for row in userGenres.collect()]
return genres
# 注册用户喜好函数
userLikesUdf = udf(userLikes, ArrayType(StringType()))
# 计算用户喜好的电影类型的TF-IDF特征向量
userRatings = ratings.groupBy("userId").agg(collect_list("movieId").alias("movieIds"))
userGenres = userRatings.withColumn("genres", userLikesUdf(col("userId")))
userGenres = userGenres.withColumn("genresStr", concat_ws(" ", "genres"))
userGenres = tokenizer.transform(userGenres)
userFeatures = hashingTF.transform(userGenres)
userFeatures = idfModel.transform(userFeatures)
# 计算电影和用户之间的余弦相似度
dot_udf = udf(lambda x, y: float(x.dot(y)), FloatType())
similarity = rescaledData.crossJoin(userFeatures).select("movieId", "userId", dot_udf("features", "features").alias("similarity"))
# 为用户推荐电影
recommendations = similarity.filter(col("userId") == 0).orderBy(col("similarity").desc()).limit(3)
recommendedMovieIds = [row.movieId for row in recommendations.collect()]
# 输出推荐结果
recommendedMovies = movies.filter(col("movieId").isin(recommendedMovieIds))
recommendedMovies.show()
```
该代码示例中使用了TF-IDF算法和余弦相似度计算,计算出了电影和用户之间的相似度,并根据相似度为用户推荐了3部电影。
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