For your customers, the best possible experience on your site is to constantly encounter items that interest and excite them. Such an experience converts returning customers into long-term fans of your business. Through next-gen machine learning techniques, our platform empowers you to deliver this extraordinary experience to your users.

Here are some of the ways you can use website personalization:

  • Curate a unique "For You" carousel for each user on your website’s home page
  • Recommend a custom list of items on product pages based on a customer’s behavioral data
  • Provide highly personalized digital content recommendations even as user filters (genre, price, location) are applied


User Interaction History

One of the way for you to evaluate your recommendation is to select a random user id within your database to see 2 set of information:

  • the ratings and taste of that user
  • the recommendations associated

You can select a random user id by clicking the icon "Pick a User Id" or enter manually a specific user Id in the search bar

The top section of the screen displays the interaction history of a specific user. You'll see a list of all the items the user has interacted with, along with the respective ratings they've given for each item. This information is vital as it represents the user's feedback, helping to contextualize the recommendations provided.

Evaluating Recommendations

In the bottom main section of the screen, you'll find the user-to-item recommendations tailored for the user in question. By default, this panel displays ten recommendations, offering a balanced overview of our recommender system's output. However, you can adjust this count according to your needs using the dropdown menu located at the top of this panel.

We offer two different views to inspect the recommendations: List View and Grid View.

  1. List View: In this detailed mode, you can see all the properties related to each recommended item. This comprehensive information can provide further insights into why these items were chosen by the system, facilitating a better understanding of our recommendation logic.
  2. Grid View: This mode presents a more visual, compact overview of the recommended items, allowing you to assess the recommendations more swiftly. The Grid View is particularly useful to get a feel of how the recommendations would appear to the user in a carousel or a grid format on your platform.

Recommendation Types

The Beam Studio dashboard is meticulously designed to help you navigate through various features smoothly. A key part of this is the right-side panel, which provides several configurable options to help you tailor your recommendations. Let's go through these features in detail:

On the right-side panel, you will notice three headers, each representing a distinct type of recommendation model that you can evaluate. The availability of these models is based on the custom setup our team has established for you:

  1. Regular: The default recommendation model, shaped by user behavior and preferences, coupled with the business rules you've implemented. This flexible model allows for extensive adjustments to tailor your recommendations.
  2. Pre-computed: Optimized for performance, this model provides pre-calculated recommendations. Due to its nature, the only adjustable parameter is the scenario; all other business rules are fixed to maintain the pre-computed model's accuracy.
  3. Context: An advanced recommendation type allowing greater customization. Alongside the standard business rules, you can define 'items' that you want the recommendations to resemble or aim towards, adding an extra layer of personalization.

Configuring Business Rules

To adjust the recommendations according to your business needs, Beam Studio provides a set of business rules that you can adapt:

  1. Scenarios: A scenario is a comprehensive set of business rules that provide a strategic framework for your recommendations. Scenarios encapsulate filters, exclusion rules, algorithms, and candidates preselection to produce a tailored recommendation system. You can select the most appropriate scenario for your business model from a predefined set, or even create custom scenarios to cater to specific business requirements. Swapping scenarios allows you to swiftly shift the complete approach of your recommendations before delving into the finer details.
  2. Algorithms: These are the parameters that are fine-tuned to select the most suitable recommendation model. They are usually set by our machine learning engineers.
  3. Filters: Use filters to refine the recommendations by including only items that fulfill certain conditions based on their properties.
  4. Diversity : This unique feature allows you to apply a diversity score to each property of a product. This score influences the variety within the recommendations, allowing you to enhance the breadth of suggestions based on specific attributes, thereby promoting a more diverse product offering.
  5. Exclude Rated Items: Ensure that the items your users have already interacted with (or rated) are not recommended again, keeping their experience fresh and engaging.

Explore Beam Studio and take advantage of these capabilities to deliver personalized user-to-item recommendations, enhancing the user experience and driving more engagement on your platform.


Given an user ID, the API will return items that match the profile of this user.

This is the most common endpoint to get recommendations for users that are already in the database, such as users who are signed in with your application. See Under the Hood to read on the underlying technologies.

The endpoint GET recommendation/users/<str:user_id>/items/ returns the recommended items ID.

Copied to clipboard!
$ curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' RUBY
$ curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' PYTHON
$ curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' PHP
$curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' JAVA
$ curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' NODE
$ curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' GO
$ curl -s \
  -H "Authorization: Bearer $JWT_TOKEN" \
  | jq -r '.items_id' NET

Get started with Crossing Minds recommendation API

Crossing Minds Recommendation API is the easiest way to integrate personalized recommendation to your website & mobile apps

Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
We use cookies (and other similar technologies) to collect data in order to improve our site. You have the option to opt-in or opt-out of certain cookie tracking technologies.To do so, click here.


API Documentation Center,
please wait a bit...