User to Attributes API

The attribute you want to make recommendations for. For example, the following query will recommend values from the brand field that Miso thinks the user will be interested in:

{"field": "brand"}

This API also works for custom attributes you define. For example, if you provide a designer custom attribute, then, you can make designer recommendations with the following query.

{"field": "custom_attributes.designer"}

Number of product recommendations to return

Number of personalized product recommendations to make for each recommended attribute. For example, the following query will return 5 products for each brand we recommend:

{
    "field": "brand",
    "products_per_attribute": 2
}

Note that a large number of products_per_attribute will increase latency slightly because we need to perform more computation for each of the recommended attributes. If you only need attribute recommendations, you can set products_per_attribute=0 to reduce latency.

The engine you want to get results from. When you have more than one engine, you can use this parameter to specify the specific engine you want to get results from. If not specified, the default engine will be used.

The user who made the query and for whom Miso will personalize the results. For an anonymous visitor, use anonymous_id instead.

The anonymous visitor who made the query and for whom Miso will personalize the results. Either user_id or anonymous_id needs to be specified for personalization to work.

The hash of user_id (or anonymous_id) encrypted by your Secret API Key. user_hash is required to prevent unauthorized API access if you are making API calls with a Publishable API Key.

You should generate the user_hash via HMAC scheme: you encrypt the desired user_id (or anonymous_id) with your Secret API Key on your backend server, and then let the front-end code send the generated user_hash to Miso APIs to verify the identity of the API caller.

As long as the Secret API Key is kept secret, the user_hash prevents a malicious attacker from making unauthorized API calls or impersonating any of your users.

Miso APIs accept the case-incentive "hex digest" of user hash, a sample Python 3 code to generate it on your backend server is as follow:

import hashlib
import hmac

YOUR_MISO_SECRET_API_KEY = "039c501ac8dfcac91"
key_bytes = YOUR_MISO_SECRET_API_KEY.encode()
user_id = "USER_123" # or anonymous_id
user_id_bytes = user_id.encode()
user_hash = hmac.new(
    key_bytes,
    user_id_bytes,
    hashlib.sha256).hexdigest()
# user_hash is "7eb04da5e..."

You can find more examples for other languages in this Github Gist

The type of products to return. Use this parameter to make the API return only a certain type of products (see Product APIs).

This is particularly useful for sites that have multiple types of products: For example, on a marketplace site, YOu may model merchandise and store as two types of products. You can then use type parameter to limit the recommendation or search results to return only one kind of them.

For instance, the following query will return only store products:

{"type": "store"}

For another example, on a travel website, you might have: hotel, thing to do, and restaurant, three kinds of products. You can use type parameter to limit results to one kind of them. For instance, the following query will limit the results to only hotels product:

{"type": "hotel"}

Whether to dedupe product based on product_group_id. If dedupe_product_group_id=true, Miso will prevent products with the same product_group_id from showing multiple times in the search or recommendation results.

This is particular useful when one product has multiple variants (for example, different sizes, colors, or materials), and you only want to show this product only once in the search or recommendation results. Miso will then return the variant that is most likely to be of the user's interest.

Defines a query in Solr syntax that can be used to restrict the superset of products to return, without influencing the overall ranking. fq can enable users to drill down to products with specific features based on different product attributes

For example, the query below limits the search results to only show products whose size is either M or S and brand is Nike:

{"fq": "size:(\"M\" OR \"S\") AND brand:\"Nike\""}

You can use fq to apply filters against your custom attributes as well. For example, the query below limits the search results to only products whose designer attribute is Calvin Klein

{"fq": "attributes.designer:\"Calvin Klein\""}

fq can also limit search results by numerical range. For example, the following query limits the results to products that have rating >= 4.

{"fq": "rating:[4 TO *]"}

Defines a query in Solr syntax that can be used to boost a subset of products to the top of the ranking, or to specific boost positions (See boost_positions parameter below.) For example, the query below will promote all the relevant products whose brand is Nike to the top of recommendation list:

{
    "boost_fq": "brand:\"Nike\""
}

For a slightly more complex example, the query below will promote the Nike products which have also been tagged as ON SALE to the top of the ranking:

{
   "boost_fq": "brand:\"Nike\" AND tags:\"ON SALE\""
}

It is worth mentioning that, Miso will only boost products that are relevant and have high likelihood to convert, and will not boost a low performance product only because it matches the boosting query.

Depending on your boosting rules, in certain cases, you would like to prevent recommendation results from being too monotone due to boosting. With Miso, you have two tools to do so.

First, you can specify boost_positions to place promoted products at specific positions in the ranking. For example, the query below will place boosted products only at the first and fourth places in the ranking (positions are 0-based), and place the remaining products in their original ranking, skipping these two positions.

{
   "boost_fq": "brand:\"Nike\" AND tags:\"ON SALE\"",
   "boost_positions": [0, 3]
}

The second tool is diversification. diversification parameter, on a best-effort basis, will try to maintain a minimum distance between products that have the same attributes. For example, the following query will place products made by the same brand apart from each other.

{
   "boost_fq": "brand:\"Nike\" AND tags:\"ON SALE\"",
   "diversification": {
       "brand": {"minimum_distance": 1}
    }
}

Name of the boosting rule. Use this to identify a boosting rule in _boosted_rules in the response