Introduction to Collaborative filtering

Fondamenti di Big Data con PySpark

Upendra Devisetty

Science Analyst, CyVerse

What is Collaborative filtering?

  • Collaborative filtering is finding users that share common interests

  • Collaborative filtering is commonly used for recommender systems

  • Collaborative filtering approaches:

    • User-User Collaborative filtering: Finds users that are similar to the target user

    • Item-Item Collaborative filtering: Finds and recommends items that are similar to items with the target user

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Rating class in pyspark.mllib.recommendation submodule

  • The Rating class is a wrapper around tuple (user, product and rating)

  • Useful for parsing the RDD and creating a tuple of user, product and rating

from pyspark.mllib.recommendation import Rating 
r = Rating(user = 1, product = 2, rating = 5.0)
(r[0], r[1], r[2])

(1, 2, 5.0)
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Splitting the data using randomSplit()

  • Splitting data into training and testing sets is important for evaluating predictive modeling

  • Typically a large portion of data is assigned to training compared to testing data

  • PySpark's randomSplit() method randomly splits with the provided weights and returns multiple RDDs

data = sc.parallelize([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
training, test=data.randomSplit([0.6, 0.4])
training.collect()
test.collect()

[1, 2, 5, 6, 9, 10]
[3, 4, 7, 8]
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Alternating Least Squares (ALS)

  • Alternating Least Squares (ALS) algorithm in spark.mllib provides collaborative filtering

  • ALS.train(ratings, rank, iterations)

r1 = Rating(1, 1, 1.0)
r2 = Rating(1, 2, 2.0)
r3 = Rating(2, 1, 2.0)
ratings = sc.parallelize([r1, r2, r3])
ratings.collect()

[Rating(user=1, product=1, rating=1.0),
 Rating(user=1, product=2, rating=2.0),
 Rating(user=2, product=1, rating=2.0)]

model = ALS.train(ratings, rank=10, iterations=10)
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predictAll()

  • The predictAll() method returns a list of predicted ratings for input user and product pair

  • The method takes in a RDD without ratings to generate the ratings

unrated_RDD = sc.parallelize([(1, 2), (1, 1)])

predictions = model.predictAll(unrated_RDD)
predictions.collect()

[Rating(user=1, product=1, rating=1.0000278574351853),
 Rating(user=1, product=2, rating=1.9890355703778122)]
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Model evaluation

rates = ratings.map(lambda x: ((x[0], x[1]), x[2]))
rates.collect()

[((1, 1), 1.0), ((1, 2), 2.0), ((2, 1), 2.0)]

preds = predictions.map(lambda x: ((x[0], x[1]), x[2]))
preds.collect()

[((1, 1), 1.000027857), ((1, 2), 1.9890355703)]

rates_preds = rates.join(preds)
rates_preds.collect()

[((1, 2), (2.0, 1.9890355703)), ((1, 1), (1.0, 1.000027857))]

The MSE is the average value of the square of (actual rating - predicted rating) 

MSE = rates_preds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
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Let's practice!

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