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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.
Historical interactions can be bulk uploaded. However, it’s essential to stream live interactions in real-time at low latency. This allows Miso to personalize the experience for each user immediately as they click, scroll, and search on your site. And it’s particularly important for new users, where we want to tailor the recommendations at cold-start. Here you’ll find some sample code for streaming interactions from your frontend JavaScript.
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.
Yes. Common examples of negative signals include: removing an item from the cart, requesting a refund, removing an item from a collection, or pressing a ‘dislike’ button. Also, an event such as a product detail view that has a very low duration may indicate that the item wasn’t that relevant to them. For more information on the different types of interactions that Miso supports, please see Importing Interaction Data.
Yes. Miso can incorporate social feedback such as liking, commenting, sharing, and rating. These are strong signals of user preference and you should send them to Miso if you have them. For more information on the different types of interactions that Miso supports, please see Importing Interaction Data.
To maximize the meaningfulness of your search and recommendation results, we recommend that you provide as many details as you can about the products or content in your catalog. This includes attributes such as the title, description, brand, and any domain-specific tags. For more information on our product schema, see Importing Product Catalog Data. When neccessary, Miso can also be trained using unstructured data, such as images and videos, to bolster the personalization models.
Yes. To use asynchronous mode, append ?async=1 to the Product or User Upload APIs.
During the onboarding phase, we will typically ask for at least 3 months of clickstream data for product detail page views and add to cart events, at minimum. Although not required, if customers’ purchase history is ready available, Miso can incorporate it into the initial training. Once the real-time clickstream data feed is set up, Miso will automatically keep track of purchase history and retrain the models accordingly on a given cadence.
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. Most interfacing with Miso is done via REST APIs in your front and back-end clients, or through our Node.js and Python SDK, primarily for data upload (content and interactions). The initial engine training and certain merchandising tools, such as synonyms, are managed through Dojo, our no-code UI for administering your Miso instance.For Answer deployments specifically, we also recommend exploring our front-end JS SDK, which provides ready-to-use UI components, built-in analytics, and customizable HTML templates, saving significant development time compared to building from scratch.
Yes. In fact, the Search and Recommendation engines are entirely separate and can be fine-tuned individually.
Absolutely! You can boost, bury, or exclude products, brands, categories, etc. from your recommendations or search results based on pre-defined conditions that are set either programmatically or using Dojo, our no-code UI. For more information, see our Boosting and Pinning recipe.