Methodology: how we score and match pets

How we turn each pet’s needs into scores and compute your fit per scenario — fully transparent and checkable.

Core principle

We do not rank pets by popularity or cuteness, but by how well each fits your real situation. To do this we break every pet’s needs into 10 dimensions, normalize them to comparable 0–100 scores, then weight the dimensions per scenario (students, apartments, beginners, etc.) to compute fit.

The 10 scoring dimensions

Normalization and direction

Each dimension maps to 0–100. For “lower-is-better” dimensions (budget, noise, cleaning load) we invert the value when matching, so “low need” correctly becomes “better for tight budgets or small spaces.” Cost dimensions also reference our per-category monthly ranges so scores track real spending.

Scenario weighting

The same pet does not fit every scenario equally. For “students” we weight budget and time more; for “apartments” we weight space and noise more; for “beginners” we weight beginner-friendliness and tolerance for mistakes. Each scenario has a weight set; we take a weighted sum of dimension scores to produce that scenario’s ranking.

You can see these rankings in action on the students、apartments and other topic pages.

Data sources

Dimension scores combine general species traits, typical per-category cost ranges, and public care guidance from veterinary associations and public-health bodies. Specific citations are on the sources page.

Updates and limitations

We periodically review scores and copy as sources update and feedback arrives. Note that scoring is a directional tool; it cannot capture individual variation, allergies, local laws or specific health conditions. It narrows the field but does not replace hands-on research and veterinary advice.

Our editorial and correction standards are on the editorial policy page.

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