A recommendation model should be understandable enough to test and expressive enough to reflect the buying decision. Tag mapping is often the right starting point. Formulas become useful when the same answer should carry different weight, several constraints interact or an invalid match must be removed before ranking.
Use tag mapping for direct relationships
In a small skincare quiz, the answer dry skin might add one point to a cream cleanser and one point to a richer moisturizer. The product with the most matching tags wins. This is fast to build, easy to explain and usually sufficient when the catalog is small and attributes do not conflict.
Use weighted scores when importance varies
A mattress quiz should not treat every answer equally. Sleep position may matter more than preferred fabric color. Assigning a larger weight to the decisive answer protects the match from being overturned by several weak preferences.
Use formulas for interacting constraints
Furniture dimensions, custom-product pricing and software plan limits often require calculations or lookup tables. A formula can combine measurements, apply thresholds and return a value that drives the recommendation. The test plan should include boundary values and conflicting inputs.
Add hard exclusions before ranking
Some answers should remove a product entirely. An allergy, unsupported integration, unavailable size or geographic restriction is not a preference. Filter impossible matches first, then score the valid set.
A simple decision rule
- Direct answer-to-product relationship: use tags.
- Different levels of importance: use weighted scores.
- Numeric, threshold or lookup behavior: use formulas.
- Safety, compatibility or availability constraint: use exclusion logic.
Use the smallest logic model that can still produce a defensible recommendation and a clear next step.
Compare the eight product recommendation quiz builders or read the full testing protocol.