A recommendation experiment should state its candidate set, ranking rule and evaluation method. Start simple enough to understand each recommendation.
Before you begin
Know Python, tabular data handling and basic statistics. Use fictional or permitted data and identify the prediction or evaluation task.
Work through the idea
Keep an experiment record with the dataset, split, baseline, metric and limitations. Choose information that would genuinely be available at the time of prediction. Compare the method with a simple alternative and inspect mistakes. A learning experiment should not be presented as a validated production decision system.
- Prepare a small interaction dataset
- Implement a baseline ranking
- Inspect errors and coverage
Practice task
Build a fictional reading-list recommender and explain three suggested items.
Check your result
Compare known-user and new-user behavior and document the evidence behind suggestions.
A mistake to avoid
Do not claim personalization when every user receives the same popularity list.
Keep a review note
Save the inputs or scenario, your reasoning and the result you checked. Explain one assumption and one case that needs more work. Revise the exercise after feedback and distinguish what you demonstrated from what remains untested.
Official reference for further study
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