User k-NN Collaborative Filtering for Item Recommendations - A step by step guide in Rapid Miner

RapidMiner

As part of a class for NYU, a team of 3 of us are building a recommendation system for books. To quickly prototype a dead simple recommender system, we put together a simple Rapid Miner workflow. You can read more about this here at Doruk Kilitcioglu’s blog. Below are is the step by step guide we used to get results from Rapid Miner for item recommendations using user-user collaborative filtering.

  1. Download ratings.csv from http://fastml.com/goodbooks-10k-a-new-dataset-for-book-recommendations/

    1. NOTE: If you don’t have an educational license with RapidMiner, you can only load in 10k rows. Open and edit the ratings file and trim it down to 10k rows.

    2. You can get an education license from the RapidMiner website if you make an account and add an .edu email

  2. Download RapidMiner and install to your machine

  3. Start a New Process and make it Blank

  4. Loading the Data

    1. Hit Add Data at the top left under repository

    2. Click on My Computer and find ratings.csv from your local machine

    3. Hit all the Next buttons and then save the file under data

    4. This might take up to a minute

    5. From the top left, expand Local Repository, then data, and then drag ratings.csv to the right window

  5. 6million ratings is too much for RapidMiner to process so let’s filter it down

    1. Find the Filter Examples operator and drag to the right window

    2. Hook up the output of Retrieve ratings to the input of Filter Examples

    3. Click on Filter Examples and click on the Add Filters button to the far right

    4. Ensure user_id is selected as the left field

    5. Make the filter operator (should be = by default) a <

    6. Type in anywhere between 500 to 1000

    7. Hit OK

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  6. Set the role of the columns

    1. Add the Set Role operator to the window

    2. Click on the box

    3. At the far right, from the attribute name drop down, select rating and set the target role to label

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    1. Click on the Edit List button

      1. Make the left field user_id and at the right field, TYPE in user identification

      2. At the bottom hit Add Entry

      3. Made the new left field book_id and at the right field, TYPE in item identification

    2. Hit Apply

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  7. Split data into train and test

    1. Add the Split Data operator to the right window

    2. Hook up output of Set Role to Split Data

    3. Click on Split Data and hit the Edit Enumeration bottom in the top right

    4. Add two entries

    5. Type in the first one as .8 (this is the train set)

    6. Type in the second one as .2 (this is the test set)

    7. Hit OK

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  8. Add Recommender System algorithm

    1. At the very top right, hit Extensions and go to the Marketplace

    2. Type Recommender in the search bar

    3. Install Recommender Extension and follow the instructions to install

  9. Add User k-NN item recommender system

    1. Find the Collaborative Filtering Item Recommendation/ User k-NN operator (will be in Extensions under Recommenders/Item Recommendation)

    2. Drag this to the right window

    3. Hook up the top output of the Split Data box to the input of the User k-NN box

  10. Apply the model to train and test

    1. Add Apply Model (Item Recommendation) operator to the right window

    2. Hook up the Mod output of the User k-NN to the input Mod of the Apply Model box

    3. Hook up the second par output of the Split Data box to the que input of the Apply Model box

    4. Drag the res output of the Apply Model box to the res on the very far right of the window (the final output)

  11. Hit the big blue Run button to view the output!

    1. The model will recommend items (books) to users based on the books other users very similar to them have read

  12. Please view the image below if you are stuck

    Item Recommendation Apply

  13. View performance metrics

    1. Delete the Apply Model box

    2. Add the Performance (Item Recommendation) operator

    3. Hook up the Mod output of the User k-NN box to the Mod input of the Performance box

    4. Hook up the exa output of the User k-NN box to the tra input of the Performance box

    5. Hook up the second par output of the Split Data box to the tes input of the Performance box

    6. Hoop up the per output of the Performance box to the res at the very far right of the window

  14. Hit the big blue Run button to view the output!

    1. The output will be a slew of performance metrics for the Item Recommendations

    2. The AUC (Area Under of the Curve) can be treated as an accuracy metric

  15. Please view the image below if you are stuck

Item Recommendation Performance

Notes and further exploration:

  1. You can use this set up on any set of ratings as long as the input csv follows the following format (User_id, item_id, rating) and you make sure to set the roles to exactly user identification, item identification and label as explained in the steps above

  2. You can predict the ratings on the test set instead of predicting good recommendations. Swap out the Item Recommendation User k-NN with Rating Prediction User k-NN if you would rather predict the ratings that users have given their books

  3. Play around with Item k-NN or other operators. These operators find items that are most similar to other items in order to make recommendations. What we used above found most similar users to other users in order to recommend items

    1. Please read more on recommender systems and techniques to make them. This post is meant to be a step by step guide for RapidMiner and not an explanation on recommender systems