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Recommendations API

This API provides document recommendations based on either the gathered data for the site or user provided data. Note: This API is in BETA

Clients calling the API are required to add an authentication header with the valid authentication token. This token can be retrieved from the Smart Search Plugin Settings.

Authorization: Bearer {ACCESS_TOKEN}

Recommendations uses cookies to collect non-identifiable data to enhance user experience through personalized recommendations. Below is a detailed overview of the cookies we use and how the data is handled.

Cookie NameDescriptionLifespanPurpose
user_idAnonymous identifier assigned to a user24 hoursPersonalization across multiple sessions
session_idAnonymous identifier for a browsing session30 minutesSession-level tracking and insights
  • Personalized Recommendations:
    We use user_id and session_id to customize content based on user behavior and interaction history.

  • Trending Content Insights:
    Aggregated data from session activity helps us identify and surface trending content to users in real time.

  • Non-identifiable Usage:
    All identifiers are anonymized and do not contain or infer personal information.

  • No personally identifiable information is collected.
  • Data is used strictly for on-site personalization and analytics.
  • We do not share this data with third parties.

The recommendations API utilizes collected site data, specifically search data, to generate a list of frequently searched documents.

Input Parameters:

  • count: the number of documents that will be returned
  • from: include documents from this date
  • to: include documents up to this date

Note: Only 7 days of searches is stored currently.

query TrendingDocuments {
recommendations(count: 2)
trendingDocuments(from: "7 days ago", to: "Now"){
docID
count
source
}
}

sample response:

{
"data": {
"recommendations": {
"trendingDocuments": [
{
"docID": "post:16",
"count": 7,
"source": {
"ID": 1,
"post_content": "Rabbit is happily running",
"post_date": "11-06-2023T12:33:00",
"post_status": "publish",
"post_title": "George the rabbit",
"post_type": "rabbit"
}
},
{
"docID": "post:21",
"count": 6,
"source": {
"ID": 2,
"post_content": "Horse in a field",
"post_date": "11-06-2023T12:33:00",
"post_status": "publish",
"post_title": "Greg the horse",
"post_type": "horse"
}
},
]
}
}
}

The recommendations API provides related documents to a user-provided document by using our AI vector search. Additionally, trending documents can be boosted in the results.

Input Parameters:

  • count: the number of documents that will be returned
  • docID: the document ID to get related documents to
  • useTrendingBy:
    • boost: boost the trending documents in the related results (if any)
  • minScore: the minimum score that a document must have to be returned
query RelatedDocuments
{
recommendations(count: 2){
relatedDocuments(docID: "post:28", useTrendingBy: {boost: true}, minScore: 0.5){
docID
score
source
}
}
}
{
"data": {
"recommendations": {
"trendingDocuments": [
{
"docID": "post:16",
"score": 0.9,
"source": {
"ID": 1,
"post_content": "Rabbit is happily running",
"post_date": "11-06-2023T12:33:00",
"post_status": "publish",
"post_title": "George the rabbit",
"post_type": "rabbit"
}
},
{
"docID": "post:21",
"score": 0.8,
"source": {
"ID": 2,
"post_content": "Horse in a field",
"post_date": "11-06-2023T12:33:00",
"post_status": "publish",
"post_title": "Greg the horse",
"post_type": "horse"
}
},
]
}
}
}