Blog
Recommendation Engine
Insights: The Next Level of Analytics in Recombee UI
Insights, the analytics section of our Admin UI, offers various predefined and fully customizable reports to track recommended items and how users interact with these recommendations.
Elevate Your Personalization Strategy with Recombee's Innovative Features
The digital landscape and customer preferences and behavior are changing faster than ever now. To help our clients stay on top of the game, our team has focused on developing innovative features...
Modern Recommender Systems - Part 2: Data
Data used by modern recommenders and how we can measure progress towards goals.
Is This Comment Useful? Enhancing Personalized Recommendations by Considering User Rating Uncertainty
Picture this: you're on the hunt for the perfect new smartphone, browsing through your favourite online electronics store. The online store’s recommendation engine pops up with what it thinks could be your possible next gadget love...
Recombeelab's 2023 Research Publications
Recombeelab, a joint research laboratory of Recombee and the Faculty of Information Technology at the Czech Technical University in Prague, experienced a highly productive year in 2023, publishing a series of insightful and impactful papers in the field of recommendation systems.
AI News and Outlook for 2024
We look at the most interesting research directions and assess the state of knowledge in key areas of AI. We'll also estimate future developments in 2024 so you know what to prepare for.
AI Assistants Know Your Preferences, Even Better Than You Do
Recommender systems and ethical controversies
The AI (R)Evolution in the Media Industry
In today's digital age, personalization has become the cornerstone of the media industry. Whether it's tailoring content recommendations, refining marketing strategies, or enhancing user experiences...
Modern Recommender Systems - Part 1: Introduction
How machine learning methods simplify item discovery and search.
Explaining Recommender Systems to Product Owners
In my presentation at the Data Technology Seminar organized by the European Brodcasting Union, I have focused on demonstrating that recommender systems can actually help public media organizations to better fulfill their role in society and reduce content distribution biases.
Inductive Matrix Completion: How to Improve Recommendations for Cold Start Users and Items by Incorporating Their Attributes
Matrix completion (MC), the problem of recovering the missing entries of a partially observed matrix, has found use in a wide range of domains. Still, its potentially most successful application is as a collaborative filtering technique for recommender systems (RSs)...
Breaking the News: The Role of AI in Modern Journalism
Artificial Intelligence (AI) has rapidly transformed the media industry in recent years. From automated news production to trend analysis and personalized content recommendations, AI has brought significant changes to the way media is created, distributed, and consumed.
Innovative Personalization Features for 2023
The digital world is changing; users' expectations for personalization are increasing, and our Recombee features are continuously improving. One of our focuses is to support our clients in providing the best possible user experiences...
Recombee Item Segmentations
Item Segmentations are Recombee's original and elegant solution to various advanced tasks related to hierarchical and relational data. The feature provides a flexible way to group items (products or pieces of content) into segments...
Bandit Models: Exploiting Popularity and Curiosity to Recommend Trending Content
Humans are inherently curious. In fact, curiosity is linked to the evolution of humankind. For instance, according to famous historian Yuval Noah Harari in his bestseller book "Sapiens", our language skills evolved as a way of gossiping...
Visual and Interactive Evaluation of Recommender Systems
When building modern real-world artificial intelligence systems, it is increasingly important to validate that the system works correctly. This is however not an easy task. Existing tools for machine learning practitioners...
Making Linear Autoencoders Work for Large Scale Recommendation Systems
Linear autoencoders for collaborative filtering in recommender systems are simple and surprisingly accurate as we explained in our blogpost on how linear methods work. The critical disadvantage of methods like EASE is that they are not applicable to real-world problems...
Linear Methods and Autoencoders in Recommender Systems
Linear regression is probably the simplest and surprisingly efficient machine learning method. It should be the method of your first choice, according to the famous KISS principle. Also, it often works better than sophisticated methods, because it is...
Recombee and Kentico Xperience: Guide to One-On-One Personalization
Recombee expanded its integration options - and now is available at the Kentico Xperience platform! Analyzing different types of personalization, we look into why Kentiko chose our AI-powered recommendation engine over manual segmentation.
Deep Learning for Recommender Systems: Next Basket Prediction and Sequential Product Recommendation
Accurate “next basket prediction” will be enabling next generation e-commerce — predictive shopping and logistics. In this blogpost, we will discuss the deep learning technology behind next basket...
Check out Our New Client-Side Integration Support and Deploy Personalized Recommendations Faster
We knew we had to bring something new to the table, when participating as a Beta Startup at Web Summit, the largest technology conference…
Machine Learning for Recommender Systems — Part 2 (Deep Recommendation, Sequence Prediction, AutoML…
In the first part of our talk, we discussed basic algorithms, their evaluation and cold start problem. Below we show how deep learning…
Machine Learning for Recommender Systems — Part 1 (Algorithms, Evaluation and Cold Start)
Recommender systems are one of the most successful and widespread application of machine learning technologies in business. There were many…
Migrating to Recombee From Microsoft Cognitive Services Recommendations
Microsoft has recently discontinued the Recommendations within the Azure Cognitive Services (MCSR). If you used this service, you are…
Personalized Push Notifications Enabled by Artificial Intelligence
Recent progress in artificial intelligence enables us to design proactive AI systems. Whereas traditional recommender systems produce…
Evaluating Recommender Systems: Choosing the Best One for Your Business
Together with the endless expansion of E-commerce and online media in the last years, there are more and more Software-as-a-Service (SaaS)…
The Value of Personalized Recommendations for Your Business
The e-commerce boom makes online environment more competitive. Internet retailers seek competitive advantages and a personalized experience…
Recommender Systems Explained
In this article, I overview broad area of recommender systems, explain how individual algorithms work.
Personalized Recommendations in Ruby
For those of you, who develop in Ruby, we prepared a simple client application enabling you to benefit from our personalized recommendations.
Artificial Intelligence in the Cloud
At Recombee, we “think big”, and prefer making big leaps in technology over taking small steps. Our team has been involved in data science and artificial intelligence research for many years. Beginning in 2012, we began to capitalize our knowledge and experience, developing products which…