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Introduction
How It Works
Prerequisites
Administrator Account and Organisation Creation Application Creation Data Set Requirements
Step 1: Data Cleanup Step 2: Data Transformation Step 3: Upload to S3 Bucket Step 4: Sharing S3 Bucket Access to Xineoh’s API
Applications, Datasets and Schemas
Applications
Application Types
Datasets and Schemas
Minimum Requirements for Datasets and Schemas Customising Datasets and Schemas
Steps in Building a Recommender with the API
Step 1: Batch Upload Step 2: Build Data and Structures for Training and Testing Step 3: Train Model Step 4: Test Model Step 5: Optimise Meta Parameters Step 6: User-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 7: Item-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count Step 8: Get Recommendations
Sample Implementation - Movielens 25M Dataset
S3 Bucket Creation Login as Organisation First Application Download MovieLens Preprocessed Data Upload Data to S3 Bucket Building the Recommender
Uploading S3 Bucket data to Xineoh Platform Build Data Train Test Optimization User Clustering and Trade-Off Item Clustering and Trade-Off
First Recommendations
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Step 8: Get Recommendations

There are multiple queries for obtaining different types of recommendations. Some of the options are:

Get Recommendations Based on User Interactions - Historical

This query gives the top item recommendations at a given moment for a given user.

You can filter out items returned based on metadata by using the ‘include’ and ‘exclude’ parameters.

For more information, please refer to the API documentation.

Get Recommendations Based on User Interactions - Session Based

This query gives the top item recommendations at a given moment for a given user.

You can filter out items returned based on metadata by using the ‘include’ and ‘exclude’ parameters.

When your data is session based you will be required to include additional session related fields. E.g. session_id.

For more information, please refer to the API documentation.

Get Similar Items

This query gives the items most likely to be interacted with given an interaction with a given item.

You can filter out items returned based on metadata by using the ‘include’ and ‘exclude’ parameters.

For more information, please refer to the API documentation.

Get Similar Users

This query gives the users most similar to a given user.

You can filter out items returned based on metadata by using the ‘include’ and ‘exclude’ parameters.

For more information, please refer to the API documentation.

Get Recommendations for a User-Cluster

This query gives the top item recommendations at a given moment for a given user-cluster.

You can filter out items returned based on metadata by using the ‘include’ and ‘exclude’ parameters.

For more information, please refer to the API documentation.

Get Recommendations for an Item-Cluster

This query gives the top item recommendations at a given moment for a given Item-Cluster.

You can filter out items returned based on metadata by using the ‘include’ and ‘exclude’ parameters.

For more information, please refer to the API documentation.

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