The User Engagement algorithm recommends products by analyzing the current user’s most recent interactions. It generates personalized recommendations based on real-time behaviors and evolving preferences.
The User Engagement algorithm tailors product recommendations based on the user’s most recent product-view activity, using a deep-learning transformer model that dynamically adapts to recent interactions, such as viewing or purchasing patterns. It focuses specifically on items the user has shown interest in during recent sessions. During a Recommendation API request to the user-engagement endpoint, the UCD profile endpoint is called to retrieve the last 10 products the user visited in the past 7 days. To receive User Engagement recommendations, the user must have at least one product visit in the last 7 days. If there have been no visits in that period, fallback recommendation results will be displayed instead.
Endpoint
GET https://recommendation.api.useinsider.com/v2/user-engagement
Visit our Postman collection to test this request.
Query Parameters
Parameter | Sample Value | Description | Data Type | Required |
|---|---|---|---|---|
partnerName | mybrand | Partner Identifier assigned by Insider One. You can use PartnerID as well. | String | Yes |
locale | us_US | Locale of requested product catalog | String | Yes |
platform | web | Requested platform. Web comes by default. | Enum | No |
userId | a1b2c3d4 | User identifier which is assigned by Insider One | String | Yes |
currency | USD | Requested currency of the products. If no value is set, the default currency in your settings is used. | String | No |
size | 50 | Required number of items in response. Valid values are 0 to 100. | Integer | No |
categoryList | [“Clothes”, “Skirts”] | Category filter of the products | Array (of string) | No |
filter | Smart Recommender filtering. There can be more than one filter parameter. | String | No | |
details | true | Adds details to the products of the response | Boolean | No |
shuffle | false | Shuffles the products of the response | Boolean | No |
getGroupProducts | false | Determines if the products within the same groupcode should be returned in the recommendation response | Boolean | No |
groupProductsFields | Defines the fields that should be returned for the products in the group_products section. | String | No | |
excludeVariants | true | Exclude variants from the response. The default value is false. Valid values are 1, 0, true, and false. | Boolean | No |
excludeViewDay | 30 | After how many days should viewed products be excluded | Integer | No (Can be used only with userId) |
excludeViewItem | 100 | How many viewed products should be excluded | Integer | No (Can be used only with userId) |
excludePurchaseDay | 30 | After how many days should purchased products be excluded | Integer | No (Can be used only with userId) |
excludePurchaseItem | 100 | How many purchased products should be excluded | Integer | No (Can be used only with userId) |
hp | false | Makes affinities affect products of the response. The default is false. | Boolean | No |
dayLimit | 2 | If FMT is published_time, it adds a day limit filter. The default is 2. | Integer | No |
productId | ABC123CBA | Current product ID | String | No |
Sample Request
The sample below displays a request to User Engagement, a personalized recommendation algorithm that analyzes the current user’s most recent interactions (such as product views, clicks, or cart actions) to deliver highly relevant product suggestions in real time.
https://recommendation.api.useinsider.com/v2/user-engagement?locale={locale}&userId={userId}&partnerName={partnerName}Sample Response
{
"success": true,
"total": 10,
"types": {
"ue": 10
},
"data": [
"649517_49890",
"568334_49053",
"639714_49677",
"651579_3255",
"614493_50094",
"614508_49668",
"568334_47380",
"641331_49914",
"621390_3255",
"646581_48990"
]
}Fallback Algorithms
If the current user has not visited two or more products, or if user engagement recommendations are filtered out, recommendations from the following algorithms are returned in sequence: