3 (2)

Following are modules that are developed in this demo:

• Sitecore Cortex (Content Tagging)

• Data Enrichment

• Data segmentation

• Training and Prediction

• Custom Personalization Rules

Sitecore Cortex (Content Tagging): From version 9.1, Sitecore provides the capability to tag items by clicking on the 'Tag Item' button available in the ribbon.

12 (1)

The Sitecore cortex content tagging makes use of third party API-Open Calais. But, somehow open Calais was not working as our requirements. So, we overrode Retrieve Content Pipeline and Get Tags Pipeline and developed our own NLP model using Azure ML studio. Following is the flow of step to develop NLP algorithm:

4 (1)

Clustering and Prediction:

Data gathering: We have used xDB interaction data and the tagging data associated with each and every item obtained from the previous step.

Data enrichment: We have the raw xDB interaction data and Sitecore item tags data. In order to get the best results by applying the machine learning algorithms, there is the a need to convert this raw noisy and untidy data to some enriched form that can be fed to models because output is directly proportional to the quality and quantity of the data.
We have pre-processed raw data, treated missing values, removed highly correlated items, transform and categorize and used One-Hot Encoding techniques for data enrichment.

Data Segmentation: In this step, we have segmented the pre-processed data by extracting the relevant features using 'feature selection' and then applying sweep clustering and Kmeans clustering.

Training: After obtaining the clustered data from the previous step, we have divided the data into two samples-training and testing, and performed model building and model tuning techniques iteratively to get the best model.

Prediction and Recommendation: The model built in the previous step is able to predict the most accurate segment/cluster to which any interaction or behavior of visitor belongs. Then, in order to perform the recommendation task, we have integrated another algorithm which was developed based on the principles of market-basket analysis.

Summary of steps:

Architecture diagram:

8 (1) Why should we use AzureML studio for machine learning tasks?

• Build for cloud-first mobile-first world

• Reduced complexity

• Collaborate work with anyone

• Visual composition, easy to use, drag and drop

• 500+ Inbuilt R Packages / Python Anaconda toolkit

• Standard ML algorithms and tools (Text mining, Regression, Classification, Clustering, Anomaly detection)

• Data sources (Azure Blob, RDBMS, Hive, Web)

• Data preparation (preprocessing, aggregation, transformation, feature engineering)

• Deploy in minutes

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