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ML in Retail: Recommendation Systems

Explore how to design feature extraction and preprocessing functions for retail recommendation systems. Understand key metrics for user and item interactions, handle sparse data, and differentiate implicit and explicit feedback. This lesson helps you prepare data for collaborative filtering and address common challenges like cold-start scenarios and interaction diversity.

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In retail recommendation systems, understanding user-item interactions is crucial for developing personalized product recommendations. Your task is to design a feature extraction and preprocessing function that:

  1. Processes raw user-item interaction data.

  2. Generates meaningful features for recommendation algorithms.

  3. Handles sparse and incomplete interaction matrices.

  4. Prepares data for collaborative filtering techniques.

Python 3.10.4
#TODO - your implementation attempt here

Sample answer

Here are our key requirements:

  1. Basic interaction analysis: Calculate fundamental metrics for the interaction matrix.

  2. User-level feature extraction: Analyze patterns in how users interact with items.

  3. Item-level feature extraction: Measure item popularity and engagement patterns.

  4. Cold-start problem indicators: Identify potential cold-start scenarios.

  5. Interaction recency handling: Track and analyze the timing of user interactions.

  6. Interaction ...