Frequently Asked Questions

Most likely! We'd love to hear your ideas. Please reach out to us any time at support@askmiso.com.

Yes! We have tons of experience in what UX patterns are most effective for personalized search and recommendations. Ask your Miso solutions engineer about Bento, our coming-soon UI Kit of best practices and style guides.

Miso will provide results based on all the activity on your site, and then will immediately learn based on the user's first few interactions. (That's why it's important to stream interactions to Miso in real time!) If you want, you can also use the user_cohort parameter to tell Miso to personalize the cold start behavior as if the user belonged to a certain cohort on your site. See the API documentation for more details.

It is essential to upload the interactions to Miso sooner rather than later. Miso's engines are trained on historical batch data, but they're also designed with the intent to supplement that knowledge with live streaming interaction data. This allows Miso to personalize the experience for the current user in real time. And it's particularly important for new users, where we want to tailor the recommendations to their interests ASAP.

We have a wide array of satellite servers in US East / Asia / Europe that serve latency-sensitive requests, such as Search and Recommendations API calls. To keep latency to an absolute minimum, some customers will choose to make Search and Recommendations requests from their front-end JavaScript code, and let their users' browsers talk to Miso API directly. Contact your Miso solutions engineer for more details about this option.

Yes! In fact, we are currently building a way for you to input synonyms directly in Dojo. For now, you can work with your Miso solutions engineer to import your list of synonyms as a spreadsheet.

Yes. To use asynchronous mode, append ?async=1 to the Product or User Upload APIs.

It's totally okay if you don't bring miso_id all the way to the checkout event and just append it to to the clickthrough event, for example. We've found this to be difficult for customers and are working on a better way to do the data collection.

At this time, we do not analyze images — only text.

We debated internally whether to always remove OUT_OF_STOCK products from the search and recommendations results. In the end, we decided not to implement a hard filter because some clients may still want to show OUT_OF_STOCK products. Although their customers can't buy those products, they can still interact with them in other ways, like adding them to a wishlist. Therefore, using fq with availability:IN_STOCK is required to remove out of stock items from the results.

Yes! When users interact with a product, the brand will also receive the same interaction "virtually" during the training. This allows us to do many cool things. For example:

1. Personalized ranking / recommendations for brands (even if users just interacted with the Products)
2. Product to Brands recommendations or vice versa.

There aren't any significant differences between these and how Miso will treat them. It is more of a semantic difference in how the organization thinks about its data internally.

In our algorithms, we avoid a negative feedback loop by occasionally randomizing our personalized results. In this way, we learn how users react to seeing less personalized results and fine-tune the models accordingly. (This randomization only occurs for a small percentage of results, to prevent an impact on the personalization performance.) Your UX can play a role in this as well. We've found that it's important to keep editorial selections on the site (e.g. on the homepage) or mix editorial selections with algorithmic selections. Recipes like Categories You May Like and Trending For You are also great for letting users explore diverse content.

Using a smaller rows parameter and fetching the subsequent results with exclude will lower the latency quite a bit. You can also make sure you're only requesting the fields that you need with the fl parameter. You can also call Miso's APIs directly from the users' browser using a user_hash and your publishable API key.

For recommendations, we expect the results to change pretty frequently because we analyze users' every click in real-time. This makes it a little tricky to support pagination as a parameter. So to enable a UI pattern like infinite scrolling, we suggest that you use the exclude parameter. You can put every product_id that has been shown on the same page in the exclude field, and then you can then get the next batch of recommendations while ensuring that there are no duplicates.

The Search API easily supports pagination. For the Search API, the results are cached for 10 minutes. During this time, the search results for the (same user + same search query) will not change.

For requests that use your publishable API key, Miso requires an encrypted user_hash, which encrypts the requested user_id (or anonymous_id) with your secret API key using HMAC. Your backend server will need to generate the user_hash, and then let the front-end code send the 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 will automatically dedupe your data and keep the one that was uploaded later. Re-uploading the same interaction or product multiple times is absolutely fine!

Yes. Personalization, especially when it's approached the wrong way and without transparency, can absolutely be creepy. However, there are a couple of ways that platforms cross the "creepy line," and we've taken care with Miso to treat this line as sacred, not cross it, and instead unlock real innovations that eliminate any temptation to cross it, because we've found a better way.

We learn the preferences of groups of users with the same attributes and use that to bootstrap the personalization. We never try to derive anything else from this data (e.g. looking into users' geographic location) or use it in any other way. User data is also much less heavily weighted than actual interactions on the site. It typically takes only 5-20 interactions for the effects of the user bootstrapping to fade and be replaced by real-time predictions based on clickstream data.

For optimal latency, Miso recommends from a "candidate set" of precomputed products that are relevant to users' interests. For example, if you are trying to filter for beauty products, and we don't have any clickstream data showing that the use has an interest in the beauty category, beauty products are less likely to be present in the candidate set.

In the case where you absolutely want to show some products regardless of users' interests, using the Search API is a better option because the default ranking will prioritize general relevancy over personalization. See the Category Pages recipe for an example.

You can check the HTTP return code to see if your request went through. Any return code >= 200 means the upload was successful.

For some data, especially related to the purchase of physical goods like groceries, customers may want to send location information. We've had success using the H3 library to convert lat/lon into hexagons with the desired resolutions. For example, a res-7 hexagon represents an area of about 5.161293 KM^2, which can be a good trade-off between precision and sparsity.  We've had good results with res-6, res-7, and res-8 so far. When sending Interaction data, you can use the context field to send us geohex information with different resolutions, along with other business-specific info (e.g. region name, city name, fulfillment center, etc.). Example:

POST https://data-api.askmiso.com/v1/interactions
{
    "data": [
    {
        "user_id": "USER_ID",
        "type": "product_detail_page_view",
        "product_ids": [
            "PRODUCT_ID"
        ],
        "context": {
            "custom_context": {
                "business_region": "us-east",
                "business_area_id": "123",
                "business_fullfillment_center": "AZ1",
                "city": "new_york",
                "geohex_7": "8a2a1072b59ffff",
                "geohex_6": "1a2a1072b59ffff",
                "weather": "raining"
            }
        }
    }
]

We are currently committed to the following integrations:

• One-click integration with Shopify (in progress)
• Integrating with Segment via Webhook
• Integrating with Split.io via Webhook

We're looking into whether we can commit to integrations with Amazon Kinesis and Google Analytics as well. For now, if you use any of these technologies, please discuss the best options with your Miso solutions engineer. We're here to help and have lots of experience guiding teams through the data upload process.

We stress test our API suite with up to 1000 QPS. But give us a note if you plan to have a QPS over 200.

We recommend picking the interactions in the conversion funnel that are most aligned with your top-line business metrics. For eCommerce, it's most likely to be a combination of add_to_cart and checkout. For content media, it can be read or watch in combination with product_detail_page_view.