A popularity model computes a static popularity score for each product or document in your index based on historical user behavior. Popularity models use sort algorithms to calculate and quantize the static quality and popularity of each item. Models are built on offline computing and work across commerce and non-commerce scenarios. Any search use case where you want frequently engaged content to surface higher in results can benefit from a popularity model.
Examples:
An e-commerce site ranks t-shirts by the number of purchases and cart additions in the last 7 days.
A content platform ranks articles by the combination of views and likes over the last 30 days.
A B2B marketplace ranks suppliers by impression count and conversion rate in the last 14 days.
Each application supports a maximum of five popularity models.
How it works
A popularity model scores each item by combining signals across four dimensions:
| Dimension | Options |
|---|---|
| Entity | Product or document, brand, merchant, leaf categories, level 1 categories |
| Time period | 1 day, 3 days, 7 days, 14 days, 30 days, time decay weighting |
| Behavior | Exposure, click, add to favorites, add to cart, purchase, comments, likes |
| Statistics | Count, number of customers, frequency, click-through rate (CTR), conversion rate |
Define each feature by picking one to two items from each dimension. The total number of features you can define equals the Cartesian product of all four dimensions.
Example features:
Number of times (statistics: count) a product (entity) received an exposure (behavior) in the last day (time period)
Sales volume (behavior + statistics) for the merchant (entity) a product belongs to, over the last 30 days (time period)
After training, the model outputs a single popularity score per item. Apply that score to a sort policy so search results reflect both relevance and real-world engagement.
Create and apply a popularity model
Three steps to use a popularity model:
Create a model.
Train the model and check the data report.
Apply the popularity score to a sort policy.
Create a popularity model
Log on to the OpenSearch console. In the left-side navigation pane, choose Search Algorithm Center > Sort Configuration. In the left pane, click Popularity Model Management, then click Create.
Enter a model name and click Submit.
After submitting, you are redirected to a confirmation page. Return to the Popularity Model Management page.
Find the model you created and click Train in the Actions column. The latest version state changes to Scheduling. Wait for training to complete.
Manage a popularity model
Popularity Model Management page
Basic Information section
View the model's creation time, current status, last training start time, and latest version state.
Configuration Information section
Scheduled Task is enabled by default, which trains the model once a day. Click Edit Scheduled Task to set a custom training cycle.
Data Verification section
This section shows the data integrity status of your application: Available Data or Abnormal Data.
The system assigns one of two integrity levels based on your data quality:
| Integrity level | Description | Upgrade condition |
|---|---|---|
| l0 | Completely unavailable. Required core fields are missing and the size of data is small, so subsequent data processing cannot be performed. | Upgrades to l1 when item page views (IPVs) exceed 100 in the last 24 hours. |
| l1 | Core fields are available and model training conditions are met. | N/A |
What's next
Algorithm surrounding — API and SDK references