# Trending items

`trending` recommendation model is used to highlight the trending (or in other workds, most popular) items in your application. But it's not just about sorting items by popularity!

Metarank can:

* combine multiple types of interactions: you can mix clicks, likes and purchases with different weights.
* time decay: clicks made yesterday are much more important than the clicks from the last months.
* multiple configurations: trending over the last week, and bestsellers over the last year.

## Configuration

A separate block in the `models` section:

```yaml
models:
  yolo-trending:
    type: trending
    weights:
      - interaction: click
        decay: 0.8 # optional, default 1.0 - no decay
        weight: 1.0 # optional, default 1.0
        window: 30d # optional, default 30 days
      - interaction: like
        decay: 0.9
        weight: 1.5
        window: 60d
      - interaction: purchase
        decay: 0.95
        weight: 3.0
      
```

The config above defines a trending model, accessible over the `/recommend/yolo-trending` [API endpoint](/reference/api.md):

* the final item score combines click, like and purchase events
* purchase has 3x more weight than click, like has 1.5x more weight than click
* purchase has less agressive time decay
* only the last 30 days of data are used for clicks and purchases, but 60 days are used for likes

## Time decay and weight

The final score used to sort the items is defined by the following formula:

```
score = count * weight * decay ^ days_diff(now, timestamp)
```

When multiple interaction types are defined, per-type scores are added together to get the final score.

Time decay configuration allows a granular control over the decaying. Here's a click importance is weighted for different `decay` values:

![decay with different options](/files/hy6ketPC0OW3Qf71bBNi)

We recommend setting decay:

* within a range of 0.8-0.95 for 1-month periods.
* within a range of 0.95-0.99 for larger periods.

See request & response formats in the [API section](/reference/api.md#recommendations).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.metarank.ai/reference/overview/recommendations/trending.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.