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What is Recombee?

Recombee, software from the company of the same name in Prague, offers an API that allows companies to generate recommendations with no discrimination towards domain or sector types, in order to provide the ease to monitor KPIs (such as CTR/CR)…

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Recent Reviews

TrustRadius Insights

Recombee has gained praise from users for its seamless integration with their platforms, allowing them to process large amounts of data …
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per month 100,000+ recommendation requests & 20,000–400,000 Active Users



per month 2,000,000+ recommendation requests & 400,000–1,000,000 Active Users



per month 5,000,000+ recommendation requests & 1,000,000–2,000,000 Active Users

Entry-level set up fee?

  • No setup fee
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  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

Starting price (does not include set up fee)

  • $99 per month 100,000+ recommendation requests & 20,000–400,000 Active Users
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Product Demos

Recombee Admin UI Demo (E-commerce Sample Database)

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Product Details

What is Recombee?

Recombee, software from the company of the same name in Prague, offers an API that allows companies to generate recommendations with no discrimination towards domain or sector types, in order to provide the ease to monitor KPIs (such as CTR/CR) more efficiently and in real-time. Recombee can generate up to 10,000 recommendations per second in numerous forms and “flavors”; home pages personalizations, related products recommendations, emailing campaigns, stored search criterias, among others.

Contrasted with rule-based personalization systems, Recombee's AI driven solution reflects real-time changes and complexity of user behavior online to drive 1:1 personalization. The solution analyzes user interactions and behavior online as well as product attributes and generates recommendations which the vendor states are more likely to spark the interest of the customer. Recombee utilizes deep-learning and collaborative filtering, as well as content-based algorithms (such as image and text processing algorithms) to ensure accurate content for all visitors.

The following is an overview of Recombee's technology platform.

Collaborative Filtering - Models based on behavioral patterns

First type of the models Recombee adds to its model ensembles are Collaborative Filtering models, which are built from collected user-item interactions, such as detail-views or purchases. By analyzing behavioral patterns across the whole userbase, the recommendations are based on extracting interactions similarities between users, items, or both. “Similar users also liked” or “others also purchased” are both examples of CF-based recommendations. Recombee uses following CF models: Matrix Factorization, Nearest Neighbor methods, and Association Rules.

Content-Based - Models based on attributes of items and users

Recombee uses Content-Based models, which estimate similarities between items or users by analyzing the provided property values. For example, two items can be considered similar by having similar categorization, name, text descriptions, etc. Various models are used to process different type of attribute data. These models are useful in cold start situations when there’s not enough interaction data yet (brand new item or user).

Deep Learning - Models combining interaction and attribute data together

Following the cutting-edge research in the field, Recombee offers models based on neural networks and deep autoencoders to build the recommendations. Such models are able to consider at once all the data provided in the given context. The models build AI-based understanding of concepts hidden in the data.

Specialized Models - Models reflecting specific business-cases and product needs

Diversification models (recommending variety of different items), popularity-based models (long-term or trending), reminder models or models periodicity-based models (based on repeating behavior in user-item interactions), are also part of Recombee. These come from vast amount of experience that Recombee team gained during years in business, applying the systems to hundreds of different use-cases.

Image Processing - Models based on analyzing images and visual similarity

Recombee can process product images to extract similarities based on visual style. This allows e.g. recommending items which are visually similar to those liked by a user in the past. Advanced models based on top of convolutional neural networks are used for that. Multiple images (such as photos taken from different angles) can be provided to further improve the performance.

AI-Based Model Optimization - Automated searching for proper hyperparametrization

The models in production need to continuously adapt to the changes in the environment such as different seasons or holidays. When put to production, the models adapt based on collected feedback. Knowing whether the recommendations really led to user actions allows Recombee to tune both the structure and the hyperparameters of the deployed model ensemble.

Recombee Video

Recombee Integration in 5 minutes

Recombee Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo
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Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Recombee has gained praise from users for its seamless integration with their platforms, allowing them to process large amounts of data quickly and efficiently. The machine learning capabilities of Recombee's engine have been highly regarded, enabling users to analyze user behaviors and make necessary adjustments to enhance the overall user experience. Customers appreciate the ease of implementation and clean documentation, which saves them valuable time and effort. With Recombee, businesses have been able to generate personalized recommendation feeds for mobile application users, resulting in increased retention and positive user metrics and feedback. Business managers have found Recombee to be beneficial in increasing customer engagement and sales by providing relevant recommendations based on individual user interactions and behavior. By solving the problem of delivering personalized recommendations without the need for developing algorithms from scratch, Recombee offers a solution that is both easy to implement and effective in improving user experiences. By utilizing powerful machine learning algorithms, Recombee ensures accurate recommendations, leading to better user experiences and potentially increased engagement and conversion rates. Users also appreciate that Recombee saves them development time while giving them precise control over product recommendations. The customizable and interactive interface has been well-received by users, making tasks easier to complete. Moreover, Recombee's ability to reduce work time and combine areas of interest has been highly valued by customers across various e-commerce solutions. It has proven successful in suggesting potential customers about a business's various offerings. The intuitive user interface provided by Recombee has allowed users to obtain follow-up statistics effectively. Both the product and support team of Recombee have been praised for their high responsiveness and knowledgeability. Personalized recommendations provided by Recombee have led to increased click-through rates and potentially increased traffic for users. Overall, Recombee has been successful in providing content and product recommendations, resulting in a significant increase in click-through rates and revenue for its users.

Easy to use API documentation: Users have found the API documentation provided by Recombee easy to understand and navigate, with everything being well-documented. Many reviewers have praised the clarity and user-friendly nature of the documentation.

Highly versatile AI-powered recommendation engine: Reviewers have highlighted the versatility of Recombee's AI-powered recommendation engine, stating that it allows for easy customization of user experiences. This feature has contributed significantly to customer satisfaction and spending, as businesses are able to tailor recommendations based on individual preferences.

Seamless integration and excellent scalability: Multiple users have mentioned that Recombee's integration is seamless and its scalability is excellent. The platform enables quick and efficient processing of large amounts of data, making it suitable for businesses dealing with high volumes of information.

Integration with Next.js: Some users have found the integration with Next.js to be confusing and time-consuming, leading to delays in implementation. They mentioned that it took them a significant amount of time to understand how to integrate Recombee with their existing Next.js system.

User interface of the dashboard: Several reviewers have mentioned that the user interface of the dashboard is not great and could benefit from improvements in terms of presentation and usability. They feel that the design and layout of the dashboard can be improved for better user experience.

Costly for startups: A number of users have expressed concerns about the cost of Recombee, especially for early-stage startups with limited funds. They feel that the pricing may be too high for smaller businesses, making it difficult for them to afford and justify using Recombee's services.

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