# Presets (default algorithms) ## What you will find here ? {% columns %} {% column width="75%" %} How dynamic contextual rules and algorithms work within **AB Tasty’s Recommendations & Merchandising platform**. It covers the six main “starting points” available when building a strategy, along with a full reference of available algorithms. Dynamic contextual rules use **real-time contextual variables** (page type, category, viewed/bought items, recency, frequency, etc.) to return relevant, up-to-date product sets for each user or page. {% endcolumn %} {% column width="25%" %}
{% endcolumn %} {% endcolumns %} ### **Items sorted by** **Definition**\ Returns products based on a chosen property or performance metric (e.g., sales, recency, margin, page views, purchased quantities).\ These are typically “Top X Products” strategies ordered by KPI and timeframe. **Logic**\ The rule aggregates catalog and event data (page views, purchases, revenue, quantities) over a defined period, ranks the products, and returns the highest-scoring ones. | Parameter | Description | Example | | ------------------ | ---------------------------------------------------- | --------------------------------------------------------------- | | **Input variable** | Global or category scope (`categoryId`, `page_type`) | `categoryId = “Shoes”` | | **Algorithm** | Aggregates metrics over X days | `pageviews_last_7_days`, `revenues_last_30_days`, `trend`, etc. | | **Output** | Ranked list of best-performing products | Top 50 bestsellers last 30 days | | **Dynamic** | Auto-updates as data evolves (daily/hourly refresh) | — | **Use cases** * *Top sellers* of the last 30 days * *Newest arrivals* (sorted by creation date) * *Trending products* (based on sales velocity ratio) * *Most relevant products* (weighted by conversion × recency × stock) *** ### **Items associated to** **Definition**\ Returns products most often purchased or viewed together with the current product (“complementary logic”).\ Built on **co-occurrence** and **TF-IDF weighting** to detect statistically meaningful associations. **Logic** | Parameter | Description | Example | | ------------------ | ------------------------------------------------------------- | ------------------------------------ | | **Input variable** | Current product ID (`productId`) or basket IDs (`cart_items`) | `"SKU12345"` | | **Algorithm** | TF-IDF based co-occurrence analysis | “Bought together”, “Viewed together” | | **Output** | Complementary product set | Printer → Ink cartridges | | **Dynamic** | Real-time refresh with new behavioral data | — | **Use cases** * *Frequently bought together* * *Cross-sell bundles* (e.g., accessories, consumables) * *Cart-based add-ons* (threshold fillers or impulse buys) *** ### **Items similar to** **Definition**\ Returns products that share strong affinities with the current one — acting as alternatives within the same universe.\ Built using **co-occurrence + TF-IDF weighting**, **attribute-based matching**, or **embedding similarity** (semantic / visual). **Logic** | Parameter | Description | Example | | ------------------ | ------------------------------------------------------ | ---------------------------------------- | | **Input variable** | Current product attributes (`productId`, `categoryId`) | `"SKU7890"` | | **Algorithm** | Similarity scoring model | “Similar products”, “Semantic”, “Visual” | | **Output** | List of substitute or style-related products | “Another Oxford shirt in blue” | | **Dynamic** | Updates as catalog and user signals change | — | **Use cases** * *Alternative products* on PDP (prevent exit) * *Style discovery* (same color / material / collection) * *Niche promotion* (long-tail visibility for new SKUs) *** ### **Items from recommendation** **Definition**\ Reuses the **output of an existing recommendation rule**.\ Used to compose multi-layered or conditional strategies. **Logic** | Parameter | Description | Example | | ------------------ | ----------------------------------------------- | ------------------------------------------- | | **Input variable** | Output of a previous strategy | “Top sellers in category” | | **Algorithm** | Sequential chaining / composition | Apply filter `stock > 0` on previous output | | **Output** | Filtered or refined set of recommended products | Top sellers with stock > 0 & margin > 20% | **Use cases** * *Composed strategies*: combine multiple rules in sequence * *Fallbacks*: use another recommendation if the first returns too few products *** ### **Manual selection** **Definition**\ A **fixed curated list** of products chosen by a merchandiser.\ Ideal for campaign or editorial content that should not change dynamically. | Parameter | Description | Example | | ------------------ | -------------------------------- | -------------------------------- | | **Input variable** | Product IDs manually selected | `[“SKU111”, “SKU222”, “SKU333”]` | | **Algorithm** | None (static list) | — | | **Output** | Always the same set until edited | “Christmas collection” | | **Dynamic** | Manual only | — | **Use cases** * *Seasonal campaigns* (e.g., Christmas, Black Friday) * *Editorial picks* (lookbooks / content curation) * *Hero products* for brand priorities or high margin *** ### **Products from variable** **Definition**\ Returns products based on a **dynamic contextual variable** from the page or user session.\ Typical examples: current category, basket content, recently viewed items, or user profile segments. | Parameter | Description | Example | | ------------------ | -------------------------------------------------- | ---------------------------------------------------------- | | **Input variable** | Dynamic contextual variable | `categoryId`, `user_recent_basket`, `last_viewed_products` | | **Algorithm** | Variable-driven lookup rule | “Best sellers in current category” | | **Output** | Contextual set of products linked to that variable | Personalized continuity | | **Dynamic** | Automatically adjusts to user context | — | **Use cases** * *Category best sellers* (using `categoryId`) * *Recently viewed products* (using `last_viewed_products`) * *Basket-based rules* (using `user_recent_basket`) * *“Because you viewed…”* personalization *** ### **Let’s deep dive into the three core algorithms powering AB Tasty Recommendations & Merchandising** Each of them plays a unique role in how products are ranked and displayed - from behavioral patterns to semantic understanding. | Algorithm | Signal Type | Data Source | Main Objective | Typical Use Cases | Link to specific documentation | | ----------------- | --------------------- | ---------------------------------- | ---------------------------------------------- | ---------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Co-occurrence** | Behavioral | Orders, sessions, views | Identify products often bought/viewed together | “Frequently bought together” | [Co-occurence documentation link](https://app.gitbook.com/o/iFKI1JaxSfPoiGt4tT2k/s/6Yw9IRJ6KbbucQPwZUCZ/~/changes/358/recommendations-and-merchandising-1/concepts/strategies/rules/ranking-blocks-default-algorithms/co-occurrence) | | **TF-IDF** | Statistical weighting | Co-occurrence + global frequency | Reduce bias from generic products | “Next book in a series” instead of “Gift card” | [TF-IFD documentation link](https://app.gitbook.com/o/iFKI1JaxSfPoiGt4tT2k/s/6Yw9IRJ6KbbucQPwZUCZ/~/changes/358/recommendations-and-merchandising-1/concepts/strategies/rules/ranking-blocks-default-algorithms/tf-idf-term-frequency-inverse-document-frequency) | | **Semantic** | Contextual (NLP) | Product text, attributes, metadata | Find products similar in meaning or style | “Other running shoes” | [Semantic Cosine Distance documentation link](https://app.gitbook.com/o/iFKI1JaxSfPoiGt4tT2k/s/6Yw9IRJ6KbbucQPwZUCZ/~/changes/358/recommendations-and-merchandising-1/concepts/strategies/rules/ranking-blocks-default-algorithms/semantic-similarity) | ### How dynamic rules and algorithms work within **AB Tasty’s Recommendations & Merchandising platform?** It covers the six main “starting points” available when building a strategy, along with a full reference of available algorithms. Dynamic contextual rules use **real-time contextual variables** (page type, category, viewed/bought items, recency, frequency, etc.) to return relevant, up-to-date product sets for each user or page. *** ### 🧩 Appendix — All Available Algorithms | Algorithm Name | Translation | Definition | | ----------------------------------- | --------------------------- | ------------------------------------------------------------------------------ | | `pageviews_last_7_days` | 7-day page views | Products with the most views over the last 7 days. | | `pageviews_last_14_days` | 14-day page views | Products with the most views over the last 14 days. | | `pageviews_last_30_days` | 30-day page views | Products with the most views over the last 30 days. | | `trend` | Trend ratio | Ratio of page views between the last and previous period (velocity indicator). | | `revenues_last_30_days` | 30-day revenue | Products generating the highest revenue over the last 30 days. | | `revenues_last_14_days` | 14-day revenue | Products generating the highest revenue over the last 14 days. | | `revenues_last_7_days` | 7-day revenue | Products generating the highest revenue over the last 7 days. | | `quantities_purchased_last_7_days` | 7-day purchased quantities | Products with the highest number of units sold in the last 7 days. | | `quantities_purchased_last_14_days` | 14-day purchased quantities | Products with the highest number of units sold in the last 14 days. | | `quantities_purchased_last_30_days` | 30-day purchased quantities | Products with the highest number of units sold in the last 30 days. | *(If your CSV contains additional specialized variants, e.g., “margin\_last\_30\_days”, they can be added under the same pattern.)* *** ### 🔄 **Data Flow & Dynamic Variable Usage** Dynamic contextual variables (see dedicated glossary) drive these recommendation rules: * **`page_type`** determines which rule is triggered (homepage vs PDP vs cart). * **`viewed_items`**, **`bought_items`**, **`cart_items`** feed “associated” and “similar” rules. * **`recency`**, **`frequency`**, and **`monetary`** score freshness and priority. * **`categoryId`**, **`productId`**, and **`user_recent_basket`** define the contextual scope for “products from variable”. Flow overview:\ `User Event (Tag)` → `Contextual Variables updated` → `Rule Evaluation (Algo)` → `Filtered Product List` → `Display on Site / Feed Analytics`.