# Concepts

- [Dynamic contextual variables](https://docs.abtasty.com/recommendations-and-merchandising/concepts/dynamic-contextual-variables.md)
- [Datasets and fields](https://docs.abtasty.com/recommendations-and-merchandising/concepts/datasets-and-fields.md): AB Tasty Recos and Merch uses datasets and fields to define the data model and interact with.
- [Enriched fields](https://docs.abtasty.com/recommendations-and-merchandising/concepts/datasets-and-fields/enriched-fields.md)
- [Calculated fields](https://docs.abtasty.com/recommendations-and-merchandising/concepts/datasets-and-fields/calculated-fields.md)
- [Formula Fields (IA)](https://docs.abtasty.com/recommendations-and-merchandising/concepts/datasets-and-fields/formula-fields-ia.md)
- [Strategies](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies.md)
- [Placement: where strategies are displayed](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/placement-where-strategies-are-displayed.md)
- [E-merchandising strategy](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/e-merchandising-strategy.md)
- [Recommendation Strategies](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/recommendation-strategies.md): With Recommendations & Merchandising (R\&M), you can anticipate visitor needs by suggesting the products that are most likely to convert. Based on onsite behavior (products viewed, purchased, or added
- [Rules](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules.md)
- [Presets (default algorithms)](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms.md)
- [Data Flow & Dynamic Variable Presets Usage](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/data-flow-and-dynamic-variable-presets-usage.md)
- [Algorithms Usage](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-usage.md)
- [Co-occurrence](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-usage/co-occurrence.md)
- [TF-IDF (Term Frequency – Inverse Document Frequency)](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-usage/tf-idf-term-frequency-inverse-document-frequency.md)
- [Semantic Similarity](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-usage/semantic-similarity.md)
- [Algorithms Appendix](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/presets-default-algorithms/algorithms-appendix.md)
- [Custom Rules](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/custom-rules.md)
- [Diversification rules](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/custom-rules/diversification-rules.md)
- [Filters](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/custom-rules/filters.md)
- [Context-based Catalog Filtering](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/custom-rules/filters/context-based-catalog-filtering.md)
- [Sorting](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/custom-rules/sorting.md)
- [Exceptions & Fallback](https://docs.abtasty.com/recommendations-and-merchandising/concepts/strategies/rules/exceptions-and-fallback.md)
- [Consent Options](https://docs.abtasty.com/recommendations-and-merchandising/concepts/consent-options.md)
- [How Merchandising Works](https://docs.abtasty.com/recommendations-and-merchandising/concepts/how-merchandising-works.md)
- [Save & Deploy logic (unified publishing)](https://docs.abtasty.com/recommendations-and-merchandising/concepts/save-and-deploy-logic-unified-publishing.md): Understand how Save & Deploy works across Recommendations and Merchandising, including draft logic, deployment lifecycle, and unified statuses.


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