Product to Products API

A list of additional interaction records. You can use this fields to simulate user interactions without actually writing them to the interaction dataset.

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.

Defines a query in Elasticsearch query-string syntax (Lucene) 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}
    }
}

Defines a list of 0-based positions you want to place the boosted products at.

For example, the query below will promote products whose brand is Nike as the top and second recommendations:

{
    "boost_fq": "brand:\"Nike\"",
    "boost_positions": [0, 1]
}

If boost_positions is not specified (which is the default behavior), all the boosted products will be ranked higher than the rest of the products.

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

Define a list of boosting rules that will be applied to the search or recommendation results simultaneously. boost_rules parameter is particularly useful when you want to boost more than one sets of products, and promote each of them to different positions. For example, the query below will promote products whose brand is Nike to the top and second results, and products whose brand is Adidas to the third and fourth results:

{
    "boost_rules": [
        {
            "boost_fq": "brand:\"Nike\"",
            "boost_positions": [0, 1]
        },
        {
            "boost_fq": "brand:\"Adidas\"",
            "boost_positions": [2, 3]
        }
    ]
}

When boosting_tags is given, and there are pre-defined boost rules have the same tag(s), those boost rules will be matched, regardless if the criteria is met or not.

Useful when want to force trigger specific boost campaign.

Whether to focus on the Products that are frequently bought together. buy_together parameter is by default false, which make the Product To Products API focus on Products that are related to the anchor products, e.g. the products with similar contents or frequently attract the interests of the same group of users.

When buy_together=true, the ProductToProducts API will focus on the type of Products that are more frequently bought together along with the anchor product(s) in the same transactions or session.

Dictionary of custom context variables for the current browsing session. You can specify context variables specific to your websites or apps in a {"KEY":VALUE} format, where KEY must be a string, and VALUE can be:

• a bool
• a string or an array of string
• a number or an array of numbers
• an array of objects
• null

In certain cases, Miso will take these variables into account when generating results.

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.

Dithering is an optional parameter (>= 1.0, and typically <= 5.0) in the recommendation APIs that introduces randomness to the order of recommended items. By adding noise to the original ranking, it shuffles the list, surfacing lower-ranked items to enhance list freshness and potentially boost user engagement. However, excessive dithering may reduce the accuracy of item ordering. See this blog post for more information.

Defines diversification rules to prevent products with the same attributes (e.g. sneakers made by the same brand or books from the same authors) from showing up too close to each other in the results.

For instance, customers who have purchased many of sneakers from Nike may happen to have recommendations or search results where all top-5 entries are sneakers made by Nike. Purely considering accuracy, these recommendations appear excellent since the user clearly appreciates Nike sneakers. However, such results might be considered too "plain" by the user, owing to its lack of diversity.

diversification parameter allows you to avoid this problem by enforcing a desired minimum distance between products. For example, consider a list of four products whose brand are Nike, Nike, Adidas, and PUMA respectively. The query below will make sure there are at least one different product between two Nike products, e.g. the diversified ranking may become Nike, Adidas, Nike, and PUMA :

{
   "diversification": {"brand": {"minimum_distance": 1}}
}

You can also increase the minimum_distance to place products further apart. For example, the following query will make sure, for the two Nike products, there are at least two other products between them. As a result, the diversified ranking may become Nike, Adidas, PUMA, and Nike.:

{
   "diversification": {"brand": {"minimum_distance": 2}}
}

The diversification algorithm reranks the products on a best-effort basis. For example, for the product list described earlier, it is not possible to place two Nike product three places apart from each other. Therefore, the diversified ranking will still remain Nike, Adidas, PUMA, and Nike* even if we set minimum_distance=3.