Oppiskele Evaluating a Recommendation Engine

Pyyhkäise näyttääksesi valikon

Evaluating recommendation systems is harder than classification or regression. You care not just about prediction accuracy but also about the quality of the ranked list of recommendations.

RMSE for Explicit Ratings

For explicit ratings (like 1–5 stars), RMSE measures how far predicted ratings are from actual ratings:


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from pyspark.sql import SparkSession
from pyspark.ml.recommendation import ALS
from pyspark.ml.evaluation import RegressionEvaluator
from pyspark.sql import Row

spark = SparkSession.builder \
    .appName("RecEval") \
    .master("local[*]") \
    .getOrCreate()

ratings_data = [
    Row(userId=1, movieId=1, rating=5.0),
    Row(userId=1, movieId=2, rating=3.0),
    Row(userId=2, movieId=1, rating=4.0),
    Row(userId=2, movieId=3, rating=5.0),
    Row(userId=3, movieId=2, rating=4.0),
    Row(userId=3, movieId=3, rating=5.0),
    Row(userId=4, movieId=1, rating=3.0),
    Row(userId=4, movieId=3, rating=4.0),
]

ratings_df = spark.createDataFrame(ratings_data)
train_df, test_df = ratings_df.randomSplit([0.8, 0.2], seed=42)

als = ALS(userCol="userId", itemCol="movieId", ratingCol="rating",
          rank=5, maxIter=10, regParam=0.1, coldStartStrategy="drop")

model = als.fit(train_df)
predictions = model.transform(test_df)

evaluator = RegressionEvaluator(metricName="rmse", labelCol="rating", predictionCol="prediction")
rmse = evaluator.evaluate(predictions)
print(f"RMSE: {rmse:.4f}")

Ranking Metrics

RMSE tells you how accurate the predicted scores are, but not whether the right items appear at the top of the recommendation list. Two additional metrics matter:

Precision@K – of the top K recommended items, what fraction did the user actually like;
Recall@K – of all items the user liked, what fraction appeared in the top K recommendations.

These require comparing the ranked recommendation list against held-out interactions, which is typically computed outside of MLlib using custom logic or libraries like ranking metrics in Spark.

A Note on Cold Start

The cold start problem occurs when a new user or item has no interaction history. ALS cannot generate meaningful recommendations for them. Common solutions are:

falling back to popularity-based recommendations for new users;
collecting a small number of initial ratings before generating personalized recommendations.

Run this locally and experiment with different rank and regParam values to see how they affect RMSE on the test set.

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