Login
Close Navigation
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
Open Navigation

Step 6: User-Clustering - Run Clustering, View Trade-Off, Choose Cluster Count

Step 6.1: Cluster Users

This API call executes the clustering using the latent factors/embeddings trained in the recommender. You select the minimum and the maximum number of user-clusters to be tested for their MSE (Mean Square Error). This will be used to choose the optimal number of user-clusters for your organisation.

For more information, please refer to the API documentation.

Step 6.2: Trade-Off User Chart

This API call gets the mean squared error for each user-cluster count between the specified min and max.

For more information, please refer to the API documentation.

Step 6.3: Choosing User Cluster Count based on MSE Results

Once you have looked at the clusters' MSE results, you can decide how many item clusters are optimal. Usually, the number of clusters where the drop-off in MSE elbows is selected.

For more information, please refer to the API documentation.

Previous Next