Mar 27
Blog
Recommendation Engine
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)...
Mar 20
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.
Mar 14
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...
Feb 17
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...
Jan 11
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...
Oct 16, 2022
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...
Sep 19, 2022
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...
Aug 29, 2022
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...
Sep 7, 2021
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.
May 6, 2021
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...
Nov 10, 2020
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…
Jan 7, 2019
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…
Jun 7, 2018
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…
Jun 3, 2018
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…
Mar 22, 2018
Personalized push notifications enabled by artificial intelligence
Recent progress in artificial intelligence enables us to design proactive AI systems. Whereas traditional recommender systems produce…
Mar 4, 2018
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)…
Dec 18, 2016
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…
Aug 23, 2016
Recommender systems explained
In this article, I overview broad area of recommender systems, explain how individual algorithms work.
Jul 12, 2016
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.
May 16, 2016
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…
Mar 30, 2016
