Machine Learning is Fun – Part 17 – Recommendation System Part 3

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Let’s start today’s discussion on product recommendation system.

Key points as apart of Exploratory Data Analysis:

  1. Collect data from source in csv or excel format or in a database.
  2. Make sure the customer and product id are well maintained in the table (perform cleansing on null rows).
  3. Make the list of most frequent items association rule with conf 0.02 ( standard form) (perform market basket analysis using apriori or ftp growth alogirthm.)
  4. Make a datalist with the query having the list of customer and recommended items which are associated strongly with the items that have been purchased by the customer before.
  5. Now we have a list of customer and recommended items.
  6. Now, make a list of products along with sentiment polarity value which has bad feedback from customer (value less than 0.1).
  7. We shall filterout that items from the previous build datalist of recommendation.

Whatever I have shared is totally my own way to build a product recommendation system. If you have any different approach, please share in the comments section below.

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