Omina Technologies collaborated with the client to build a hybrid AI recommendation engine. This type of design is considered the industry standard. The word ´hybrid´ refers to the fact that we match the client’s user base with a tailored set of recommendations by clustering both customers as well as media content. Our machine learning algorithms provide for each customer cluster and content cluster a preference score, which in turn is used to make content recommendations.
To ensure that the AI recommendation engine is GDPR compliant by design, both anonymization and pseudoanonymization are embedded within the platform’s core design.
- Anonymization: Internally, Omina uses a custom-made database (in PostGreSql) making sure that any external data referring to end-users provided by the client is encrypted and anonymous.
- Pseudoanonymization: GDPR-compliant by design, the recommendations are computed not on the individual user level, but on the level of groups of users (based on their location, the company they work for and a psychological profile test). This ensures that the principle of pseudoanomization is respected.
By default, the recommendation engine will serve similar content to users within the same cluster. To enable customization of the content recommendation for users within a cluster, Omina Technologies collaborates with the client to implement some business rules on top of the machine learning content recommendations. For example, users having exclusive access to particular media content can be served this content in that way.
The product is designed using a microservices architecture.
In the first stage, the product is built in a local environment, where each microservice corresponds to a Docker container. To store the necessary data for the recommendation pipeline, a Postgresql database was constructed. The client can communicate with this database through an API that was built using Flask. The recommendation engine itself uses a neural network design through Keras.
In the second stage, the product will run natively in the cloud through Google Cloud Platform using their storage and compute services. That way Omina effectively and efficiently accounts for the total cost of ownership and security of the services.
Particular attention is also given to quality assurance, making sure that the Python code follows PEP8 standards, passes the necessary unit tests, respects the necessary static type checking and security concerns.