# Presets (default algorithms)
## What you will find here ?
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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.
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### **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`.
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