> ## Documentation Index > Fetch the complete documentation index at: https://store-docs.quizify.io/llms.txt > Use this file to discover all available pages before exploring further. # Normal (Answer-Based) Recommendations > Explains the Normal recommendation type in Quizify — answer options are directly linked to products, variants, collections, or product tags that appear on the result page when that answer is selected. Normal recommendations are the simplest and most commonly used recommendation type in Quizify. You link individual answer options to products, variants, collections, or product tags, and those items appear on the result page when a customer selects that answer. No scoring, no types — just a direct connection between an answer and what to recommend. *** ## How It Works When you set up Normal recommendations, you attach one or more products, variants, collections, or product tags to each answer option in your quiz. When a customer selects an answer, the items linked to that answer are added to their result set. When the quiz ends, all the collected products are displayed together on the result page. The logic is straightforward: selected answer → linked products → result page. ### What Happens When a Customer Selects Multiple Answers Most quizzes include questions that allow customers to select more than one answer. When multiple answers are selected, Quizify takes the union of all linked products — meaning the result page shows every product linked to any of the selected answers, with duplicates removed. For example, if Answer A links to Product 1 and Product 2, and Answer B links to Product 2 and Product 3, a customer who selects both A and B will see Products 1, 2, and 3 on their result page — with Product 2 appearing only once. *** ## Best Use Cases Normal recommendations work well when: * Each answer clearly maps to a specific product or set of products * Your product catalogue is small to medium in size * You want a fast, simple setup without complex logic * The right product can be determined by individual answers rather than an overall score or category Examples include a skincare finder where each concern maps to a specific product, a haircare quiz where hair type answers link to matching ranges, or a supplements quiz where specific health goals point to relevant products. *** ## Normal vs. Score-Based vs. Type-Based | Feature | Normal | Score-Based | Type-Based | | - | - | - | - | | How it works | Answers link directly to products, variants, collections, or tags | Answers add points to a total score | Answers vote for a result type | | Best for | Direct product matching | Spectrum outcomes | Personality / category outcomes | | Setup complexity | Low | Medium | Medium | Normal recommendations are the best starting point for most quizzes. Switch to score-based or type-based only if your product logic requires a more sophisticated approach.

You can use exact match or partial match mode to control how strictly products are filtered when customers make multiple selections. See the Exact Match and Partial Match Recommendations guide for details.

 This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.