Do I need to use machine learning for recommendations or are rules enough?

Not always. Rules are often enough for recommendations if your needs are simple and predictable. Machine learning helps when you need personalization at scale or when the best recommendation depends on lots of behavior data.

Rules are enough when:

• You have a small catalog
• Recommendations are obvious or deterministic
• You want full control and explainability
• You’re just starting out and have little user data

Examples:

• “Show related products from the same category”
• “Recommend best sellers”
• “If user bought X, suggest Y”
• “Show the latest or most popular items”

Machine learning is better when:

• You have many users/items
• User preferences are different and dynamic
• You have enough interaction data: clicks, purchases, ratings, watch time
• You want to learn patterns that are hard to encode manually

Examples:

• Personalized homepage feeds
• “People like you also liked…”
• Ranking products by likelihood to convert
• Adapting to changing user interests over time

A practical approach:

Start with rules first, then add ML if:

• users ignore the recommendations,
• the catalog grows large,
• you need better personalization,
• or the business impact justifies the complexity.

A common setup is:

1. Rules for a baseline
2. ML for ranking/personalization
3. Rules as safety/fallbacks (e.g. exclude out-of-stock items)

If you want, I can help you decide based on your specific product, data, and scale.