Explore the Most Asked Questions

A recommender system is a technology that is deployed in the environment where items (products, movies, events, articles) are to be recommended to users (customers, visitors, app users, readers) or the opposite. Typically, there are many items and many users present in the environment making the problem hard and expensive to solve.

Check our blog post explaining the topic in further detail here.

By default, content-based models (text processing, image processing, and other types of meta-data) are turned for the cold start user or item with no prior interactions. As soon there is at least one interaction from the user or with the item, personalized models start to being used. This way our solution seamlessly tackles the cold-start problem.

Yes. We maintain a priority list of new features based on customer suggestions. We are quite strong in research, innovations, and inventing new functionality is our passion. Check out our articles on the Recombee blog to find out more about recently launched inverse recommendations and other features.

Yes. Definitely.

Anonymous users are very important and Recombee is built to learn from them and adapt in real-time as they make interactions. You can use whatever unique identifier for userID, such as the session ID, or GA clientID. You can learn more here.

We never share data with third parties, neither we share data between individual clients (there is no cross-database data enrichment).

Yes.

It’s fine to have missing fields if the items do not have such properties by their nature. These items should be still recommendable.

Yes, they do. Recombee solution is composed of a mixture of models, including collaborative filtering and content-based models. While collaborative filtering has a higher weight/priority, some of the recommendations are generated with the help of the content-based models (based on the item and user properties).

Collaborative filtering algorithms are used when a large portion of the items is covered with interactions (having at least tens of interactions).

Tracking users is up to you. We only require a unique string ID for each of the users. Please note, Recombee is fully GDPR compliant.

Besides item properties and user interactions, user properties can be also used for producing recommendations. Sending user properties is optional. User properties are used mainly for new (cold-start) users without any previous interactions. When the user has interactions, the results produced by more sophisticated collaborative filtering models are preferred as they usually have better performance.

They are taken into consideration according to the rotationRate and rotationTime parameters of the recommendation requests.

Yes, we have functionality using the logic parameter for the recommendation endpoints in our API. You can use logic=recombee:popular for this. See more here.

If you have some items that you wish to recommend with less probability return e.g. 0.3 for them while you return 1 for the others in the booster.

The minimum is 0 and the maximum is not exactly defined, but the greater number the booster returns, the greater the boost is. You can test which constants work the best according to your preferences.

Unlimited. :) In the Free Plan, the maximum amount of databases you can have is two. If you’d like to have more, we would recommend upgrading your plan.

You can erase all the contents by using the Reset Database option in the settings section of your database in the Admin UI.

Yes, we can take user’s direct input such as: Preferred categories, Topics of interest, Selected geolocation, Search Query (Semantic/Full-text) into account.

The input can be sent as the user properties (metadata) or with each recommendation request, based on your preference.

Yes, Recombee is a real-time recommender system.

The interaction is processed within a few milliseconds and is taken into consideration on the very next recommendation request.

Yes, as long as we get the geo-information (e.g. as latitude/longitude or a city + state) we are able to provide both national and local trending content.

The geo-information can be provided to Recombee either with each interaction and recommendation request or as a user property, based on your convenience.

Yes, we are capable of taking device type into the consideration as slightly different type of content is being consumed across different devices.

A vast majority of the models are fully real-time with incremental training.

Therefore the users can enjoy recommendations that immediately react to their interactions, as well as instant discovery of breaking news.

Models that require batch processing (e.g. neural models that work on top of the article texts and images) are periodically retrained automatically. There is no need to trigger training manually.

No, Recombee can compute analytics (such the trending articles) from the received interactions.

There is no need to manually maintain the models, the models auto-update.

A vast majority of the models are fully real-time with incremental learning.

Models that require batch processing (e.g. some neural models that work on top of the article texts) are periodically retrained automatically.

Recombee thoroughly monitors that the models are up-to-date.

The content is incorporated immediately.

Our reinforcement learning models can recognize the breaking news in record time and show them to users at prominent positions within the recommendations.