Collaborative Filtering on Amazon Products With PySpark

Goals

Data set description

This is a list of over 34,000 consumer reviews for Amazon products like the Kindle, Fire TV Stick, and more provided by Datafiniti's Product Database. The dataset includes basic product information, rating, review text, and more for each product.
Note that this is a sample of a large dataset. The full dataset is available through Datafiniti.

from pyspark.sql import SparkSession, column

spark = SparkSession.builder.appName('rs').getOrCreate()

df = spark.read.csv('Amazon_Consumer_Reviews.csv', inferSchema=True, header=True)

df.printSchema()

import re
from pyspark.sql.functions import col

def rename_cols(df):
    for column in df.columns:
        new_column = column.replace('.','')
        df = df.withColumnRenamed(column, new_column)
    return df

df_2 = rename_cols(df)

df_2.columns

['id', 'dateAdded', 'dateUpdated', 'name', 'asins', 'brand', 'categories', 'primaryCategories', 'imageURLs', 'keys', 'manufacturer', 'manufacturerNumber', 'reviewsdate', 'reviewsdateSeen', 'reviewsdidPurchase', 'reviewsdoRecommend', 'reviewsid', 'reviewsnumHelpful', 'reviewsrating', 'reviewssourceURLs', 'reviewstext', 'reviewstitle', 'reviewsusername', 'sourceURLs']

df_3 = df_2.select('reviewsusername', 'id', 'reviewsrating')

df_3.groupBy('reviewsusername').count().orderBy('count', ascending=False).show(10)

+-----------------+-----+ | reviewsusername|count| +-----------------+-----+ |ByAmazon Customer| 889| | Mike| 62| |ByKindle Customer| 45| | Dave| 44| | Chris| 38| | John| 30| | Nana| 28| | Anonymous| 27| | ByMike| 26| | Brad| 26| +-----------------+-----+ only showing top 10 rows

df_3.groupBy('reviewsrating').count().orderBy('count', ascending=False).show(10)

+-------------+-----+ |reviewsrating|count| +-------------+-----+ | 5|19831| | 4| 5621| | 3| 1205| | 1| 965| | 2| 617| | 0| 91| | 16| 1| | 44| 1| +-------------+-----+

df_4 = df_3.filter("reviewsrating != 44 AND reviewsrating != 16")

from pyspark.sql.functions import *
from pyspark.ml.feature import StringIndexer, IndexToString

stringIndexer = StringIndexer(inputCol="id", outputCol="id_int")
model = stringIndexer.fit(df_4)
df_5 = model.transform(df_4)

df_5.groupBy('id_int').count().orderBy('count', ascending=False).show(10)

+------+-----+ |id_int|count| +------+-----+ | 0.0| 8343| | 1.0| 3728| | 2.0| 2443| | 3.0| 2370| | 4.0| 1676| | 5.0| 1425| | 6.0| 1212| | 7.0| 1024| | 8.0| 987| | 9.0| 883| +------+-----+ only showing top 10 rows

stringIndexer = StringIndexer(inputCol="reviewsusername", outputCol="userid")
model = stringIndexer.fit(df_5)
df_6 = model.transform(df_5)

df_6.show(5)

+----------------+--------------------+-------------+------+-------+ | reviewsusername| id|reviewsrating|id_int| userid| +----------------+--------------------+-------------+------+-------+ | Byger yang|AVpgNzjwLJeJML43Kpxn| 3| 0.0|12659.0| | ByMG|AVpgNzjwLJeJML43Kpxn| 4| 0.0| 153.0| |BySharon Lambert|AVpgNzjwLJeJML43Kpxn| 5| 0.0|11560.0| | Bymark sexson|AVpgNzjwLJeJML43Kpxn| 5| 0.0|12926.0| | Bylinda|AVpgNzjwLJeJML43Kpxn| 5| 0.0| 953.0| +----------------+--------------------+-------------+------+-------+ only showing top 5 rows

train, test = df_6.randomSplit([0.75,0.25])

train.count()

21251

test.count()

7079

from pyspark.ml.recommendation import ALS

rs = ALS(maxIter=10, regParam=0.01, userCol='userid', itemCol='id_int', ratingCol='reviewsrating', nonnegative=True, coldStartStrategy="drop")
rs = rs.fit(train)

pred = rs.transform(test)

RMSE is roughly 1.22, which is quite satisfied for a small dataset of 21000 training rows. Lower RMSE can be achieved with a hybrid recommender system.

from pyspark.ml.evaluation import RegressionEvaluator

evaluator = RegressionEvaluator(metricName='rmse', predictionCol='prediction', labelCol='reviewsrating')
rmse = evaluator.evaluate(pred)
print(rmse)

1.2034306578134335

# find out which products userid has used
total_prod = df_6.select('id_int').distinct()
used_prod = df_6.filter(df_6['userid'] == 20).select('id_int').distinct()
used_prod = used_prod.withColumnRenamed("id_int", "id_int_used")
joined = total_prod.join(used_prod, total_prod.id_int == used_prod.id_int_used, how='left')

new_prod = joined.where(col('id_int_used').isNull()).select(col('id_int')).distinct()
new_prod = new_prod.withColumn("userid",lit(int(20)))
new_prod.show(5)

+------+------+ |id_int|userid| +------+------+ | 8.0| 20| | 7.0| 20| | 49.0| 20| | 29.0| 20| | 64.0| 20| +------+------+ only showing top 5 rows

# run ALS model and pick top 5 products
rec = rs.transform(new_prod).orderBy('prediction', ascending=False)
rec.createTempView('rec')
rec_5 = spark.sql('SELECT id_int FROM rec LIMIT 5')
prod_id = rec_5.join(df_6, rec_5.id_int == df_6.id_int, how='left')

prod_id.select('id').join(df_2, prod_id.id == df_2.id, how='left').select('name').distinct().show()

Collaborative Filtering on Amazon Products With PySpark

Goals

Recommend top 5 products for an user: RMSE = 1.22

Data set description

RMSE is roughly 1.22, which is quite satisfied for a small dataset of 21000 training rows. Lower RMSE can be achieved with a hybrid recommender system.

Top 5 recommend products userid 20