Collaborative Filtering in Pyspark

Collaborative Filtering in Pyspark

Collaborative Filtering in Pyspark. An introduction to Collaborative Filtering and implementation in Pyspark using Alternating Least Squares (ALS) algorithm

Have you ever wonder how Spotify is able to put up a list of songs every week in “Discover Weekly” and you ended up adding some of these songs to your playlists because you like them? What about the those shows recommended for you by Netflix because you watched a particular show yesterday? How are these tech giants so smart? The answer is Recommender System.

A Recommender System makes prediction based on users’ historical behaviours like view, search or purchase histories. Two common approaches of recommender system are Content Basedand** Collaborative Filtering**, and this article will dive deeper into the latter.

What is Collaborative Filtering?

Collaborative Filtering is a mathematical method to find the predictions about how users can rate a particular item based on ratings of other similar users. Typical Collaborative Filtering involves 4 different stages:

  1. Data Collection — Collecting user behaviours and associated data items
  2. Data Processing — Processing the collected data
  3. Recommendation Calculation — Calculate referrals based on processed data
  4. Result Derivation — Extract the similarity and return the top N results

data-science recommendation-system spark collaborative-filtering machine-learning

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