Review and Provide Feedback on Alternative Recommendation Engine

[JohnsL-U]

Description: I have developed a new recommendation engine model address previous issues with database compatibility and computational logic.

Assumptions:

• The model's data is sourced from our existing PostgreSQL database.
• The scoring mechanism includes label similarity, location proximity, and checks for likes by followed users.
• The scores are normalized to enhance variability and differentiation.
• 'story_labels' is a list of labels for each story

Tasks:

• [x] Review the Updated Model: Examine the changes made to the scoring logic.
• [x] Test the Model: Run the model with various inputs to assess its performance and the diversity of its recommendations.
• [x] Provide Feedback: Share any observations about the model's performance, including any potential issues or areas for improvement.

Notes:

• You can test by utilizing Postman. Simply send GET request at http://127.0.0.1:5002/recommendations?user_id=3
  The hard coded data allows use to test up to 10 users. Please see the attached image.

[JohnsL-U]

On my next commit, I will update the code with database fetch functions!

[JohnsL-U]

Updated recommendation engine V2 for postgre sql data fetching. Upon completion of issue #92 you can test the entire system. For the moment, simply comment out Database Functions and use the dummy data!

[JohnsL-U]

99 Needs to be resolved before the test can be performed through the app with real data.

[JohnsL-U]

More features added, bugs fixed. The web app was deployed on local and tests conducted with real data were successful.