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SARplus

Pronounced surplus as it's simply better if not best!

Build Status PyPI version

Simple Algorithm for Recommendation (SAR) is a neighborhood based algorithm for personalized recommendations based on user transaction history. SAR recommends items that are most similar to the ones that the user already has an existing affinity for. Two items are similar if the users that interacted with one item are also likely to have interacted with the other. A user has an affinity to an item if they have interacted with it in the past.

SARplus is an efficient implementation of this algorithm for Spark.

Features:

  • Scalable PySpark based implementation
  • Fast C++ based predictions
  • Reduced memory consumption: similarity matrix cached in-memory once per worker, shared accross python executors

Benchmarks

# Users # Items # Ratings Runtime Environment Dataset
2.5mio 35k 100mio 1.3h Databricks, 8 workers, Azure Standard DS3 v2 (4 core machines)

Top-K Recommendation Optimization

There are a couple of key optimizations:

  • map item ids (e.g. strings) to a continuous set of indexes to optmize storage and simplify access
  • convert similarity matrix to exactly the representation the C++ component needs, thus enabling simple shared, memory mapping of the cache file and avoid parsing. This requires a customer formatter, written in Scala
  • shared read-only memory mapping allows us to re-use the same memory from multiple python executors on the same worker node
  • partition the input test users and past seen items by users, allowing for scale out
  • perform as much of the work as possible in PySpark (way simpler)
  • top-k computation ** reverse the join by summing reverse joining the users past seen items with any related items ** make sure to always just keep top-k items in-memory ** use standard join using binary search between users past seen items and the related items

Image of sarplus top-k recommendation optimization

Usage

Python

from pysarplus import SARPlus

# spark dataframe with user/item/rating/optional timestamp tuples
train_df = spark.createDataFrame([
    (1, 1, 1), 
    (1, 2, 1), 
    (2, 1, 1), 
    (3, 1, 1), 
    (3, 3, 1)], 
    ['user_id', 'item_id', 'rating'])

# spark dataframe with user/item tuples
test_df = spark.createDataFrame([
    (1, 1, 1), 
    (3, 3, 1)], 
    ['user_id', 'item_id', 'rating'])

model = SARPlus(spark, col_user='user_id', col_item='item_id', col_rating='rating', col_timestamp='timestamp')
model.fit(train_df, similarity_type='jaccard')


model.recommend_k_items(test_df, 'sarplus_cache', top_k=3).show()

# For databricks
# model.recommend_k_items(test_df, 'dbfs:/mnt/sarpluscache', top_k=3).show()

Jupyter Notebook

Insert this cell prior to the code above.

import os

SUBMIT_ARGS = "--packages eisber:sarplus:0.2.6 pyspark-shell"
os.environ["PYSPARK_SUBMIT_ARGS"] = SUBMIT_ARGS

from pyspark.sql import SparkSession

spark = (
    SparkSession.builder.appName("sample")
    .master("local[*]")
    .config("memory", "1G")
    .config("spark.sql.shuffle.partitions", "1")
    .config("spark.sql.crossJoin.enabled", True)
    .config("spark.ui.enabled", False)
    .getOrCreate()
)

PySpark Shell

pip install pysarplus
pyspark --packages eisber:sarplus:0.2.6 --conf spark.sql.crossJoin.enabled=true

Databricks

One must set the crossJoin property to enable calculation of the similarity matrix (Clusters / < Cluster > / Configuration / Spark Config)

spark.sql.crossJoin.enabled true
  1. Navigate to your workspace
  2. Create library
  3. Under 'Source' select 'Maven Coordinate'
  4. Enter 'eisber:sarplus:0.2.5' or 'eisber:sarplus:0.2.6' if you're on Spark 2.4.1
  5. Hit 'Create Library'
  6. Attach to your cluster
  7. Create 2nd library
  8. Under 'Source' select 'Upload Python Egg or PyPI'
  9. Enter 'pysarplus'
  10. Hit 'Create Library'

This will install C++, Python and Scala code on your cluster.

You'll also have to mount shared storage

  1. Create Azure Storage Blob

  2. Create storage account (e.g. )

  3. Create container (e.g. sarpluscache)

  4. Navigate to User / User Settings

  5. Generate new token: enter 'sarplus'

  6. Use databricks shell (installation here)

  7. databricks configure --token 4.1. Host: e.g. https://westus.azuredatabricks.net

  8. databricks secrets create-scope --scope all --initial-manage-principal users

  9. databricks secrets put --scope all --key sarpluscache 6.1. enter Azure Storage Blob key of Azure Storage created before

  10. Run mount code

dbutils.fs.mount(
  source = "wasbs://sarpluscache@<accountname>.blob.core.windows.net",
  mount_point = "/mnt/sarpluscache",
  extra_configs = {"fs.azure.account.key.<accountname>.blob.core.windows.net":dbutils.secrets.get(scope = "all", key = "sarpluscache")})

Disable annoying logging

import logging
logging.getLogger("py4j").setLevel(logging.ERROR)

Development

See DEVELOPMENT.md for implementation details and development information.