A Node.js client (SDK) for easy use of the Recombee recommendation API. If you don't have an account at Recombee yet, you can create a free account here.
Documentation of the API can be found at docs.recombee.com.
For client side (browser, mobile apps ...) .js library please see this repository.
npm i recombee-api-client --save
The SDK supports both Promises and callbacks, so you can choose the way which suits your coding style and conventions of your project:
//Using Promise
client.send(new AddDetailView)
.then((response) => {
//handle response
})
.catch((error) => {
//handle error
});
//Using callback
client.send(new AddDetailView,
(error, response) => {
//handle result
}
);var recombee = require('recombee-api-client');
var rqs = recombee.requests;
var client = new recombee.ApiClient('--my-database-id--', '--db-private-token--');
// Prepare some userIDs and itemIDs
const NUM = 100;
var userIds = Array.apply(0, Array(NUM)).map((_, i) => {
return `user-${i}`;
});
var itemIds = Array.apply(0, Array(NUM)).map((_, i) => {
return `item-${i}`;
});
// Generate some random purchases of items by users
const PROBABILITY_PURCHASED = 0.1;
var purchases = [];
userIds.forEach((userId) => {
var purchased = itemIds.filter(() => Math.random() < PROBABILITY_PURCHASED);
purchased.forEach((itemId) => {
purchases.push(new rqs.AddPurchase(userId, itemId, {'cascadeCreate': true}))
});
});
// Send the data to Recombee, use Batch for faster processing of larger data
client.send(new rqs.Batch(purchases))
.then(() => {
//Get 5 recommended items for user 'user-25'
client.send(new rqs.RecommendItemsToUser('user-25', 5))
.then((recommended) => {
console.log("Recommended items for user-25: %j", recommended);
});
})
.catch((error) => {
console.error(error);
// Use fallback
});var recombee = require('recombee-api-client');
var rqs = recombee.requests;
var client = new recombee.ApiClient('--my-database-id--', '--db-private-token--');
const NUM = 100;
// We will use computers as items in this example
// Computers have four properties
// - price (floating point number)
// - number of processor cores (integer number)
// - description (string)
// - image (url of computer's photo)
// Add properties of items
client.send(new rqs.Batch([
new rqs.AddItemProperty('price', 'double'),
new rqs.AddItemProperty('num-cores', 'int'),
new rqs.AddItemProperty('description', 'string'),
new rqs.AddItemProperty('time', 'timestamp'),
new rqs.AddItemProperty('image', 'image')
]))
.then((responses) => {
//Prepare requests for setting a catalog of computers
var requests = Array.apply(0, Array(NUM)).map((_, i) => {
return new rqs.SetItemValues(
`computer-${i}`, //itemId
//values:
{
'price': 600 + 400 * Math.random(),
'num-cores': Math.floor(Math.random() * 8) + 1,
'description': 'Great computer',
'time': new Date().toISOString(),
'image': `http://examplesite.com/products/computer-${i}.jpg`
},
//optional parameters:
{
'cascadeCreate': true // Use cascadeCreate for creating item
// with given itemId, if it doesn't exist
}
);
});
//Send catalog to the recommender system
return client.send(new rqs.Batch(requests));
})
.then((responses) => {
// Generate some random purchases of items by users
var userIds = Array.apply(0, Array(NUM)).map((_, i) => {
return `user-${i}`;
});
var itemIds = Array.apply(0, Array(NUM)).map((_, i) => {
return `computer-${i}`;
});
// Generate some random purchases of items by users
const PROBABILITY_PURCHASED = 0.1;
var purchases = [];
userIds.forEach((userId) => {
var purchased = itemIds.filter(() => Math.random() < PROBABILITY_PURCHASED);
purchased.forEach((itemId) => {
purchases.push(new rqs.AddPurchase(userId, itemId, {'cascadeCreate': true}))
});
});
// Send purchases to the recommender system
return client.send(new rqs.Batch(purchases));
})
.then((responses) => {
// Get 5 recommendations for user-42, who is currently viewing computer-6
// Recommend only computers that have at least 3 cores
return client.send(new rqs.RecommendItemsToItem('computer-6', 'user-42', 5,
{'filter': "'num-cores' >= 3"}
));
})
.then((recommended) => {
console.log("Recommended items with at least 3 processor cores: %j", recommended);
// Recommend only items that are more expensive then currently viewed item (up-sell)
return client.send(new rqs.RecommendItemsToItem('computer-6', 'user-42', 5,
{'filter': " 'price' > context_item[\"price\"] ",
'returnProperties': true}
));
})
.then((recommended) => {
console.log("Recommended up-sell items: %j", recommended)
// Filters, boosters and other settings can be set also in the Admin UI (admin.recombee.com)
// when scenario is specified
return client.send(new rqs.RecommendItemsToItem('computer-6', 'user-42', 5,
{'scenario': "product_detail"}
));
})
.then((recommended) => {
// Perform personalized full-text search with a user's search query (e.g. "computers")
return client.send(new rqs.SearchItems('user-42', 'computers', 5));
})
.then((matched) => {
console.log("Matched items: %j", matched)
})
.catch((error) => {
console.error(error);
// Use fallback
});Various errors can occur while processing request, for example because of adding an already existing item or submitting interaction of nonexistent user without cascadeCreate set to true. These errors lead to the ResponseError, which is thrown or put to callback function by the send method of the client (depending on using Promises or callbacks). Another reason for errorneous request is a timeout. ApiError is the base class of both ResponseError and TimeoutError.
We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since problems can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.