This markdown file will present the proposed design, language, technological tools, and graph design for the Serv Upsell Recommendation Graph.
First, Serv will need to design an algorithim fast, using weighted nodes calculated by the Lift between two different menu items. Lift is determined using a mathematical equation defined by:
This will also be subsidized by a small Cosine Similarity embedding to define the actual structural similarity between items: this helps determine if items are more appetizer focused, which ones may be main meals, etc. the graph will be designed in Python at first for extremely fast development, where it will be tested against past customer order patterns. We'll design a Computer Vision Model to recognize the license plates so we can match previous customers accordingly, and then input them through a data validation model where we verify the customers past orders.
We also need a continuous Weight Updater where the weights are redefined each day with an alpha of .005 for steady reintegration using the following equation:
Example Graph:
Next, we need to continually test the graph using Backtesting where we measure the Mean Reciprocal Rate (MRR) compared to past customers ordering patterns. This ensures that the graph is actually accurate over time and doesn't begin to degrade.
After we reach an 85-90% accuracy metric, we then proceed to deploy with AWS alongside either Kafka or AWS SQS.
After that, we run A/B tests with customers using the upsell logic and recommending random upsells to test how effective the recommendations are. If they're inaccurate or don't produce substantive changes within a 95% confidence interval, we re-design the weights of the graph and try again.
The Python algorithms would be rewritten in Rust to keep performance optimal and to ensure that the Upsell logic is ready to activate as soon as the client enters through the door. To ensure that we never experience server disruption, distributed databases like CockroachDB or NoSQL databases such as AWS DynamoDB will help with extremely fast data lookups and even performance across different geological positions.