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ML in Retail: Recommendation Systems

ML in Retail: Recommendation Systems

Explore how to create personalized recommendations using collaborative filtering and matrix factorization techniques.

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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.

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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 diversity analysis: Measure the variety in user engagement patterns. ...