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The new standard in recommendation accuracy.
Tamber makes mainstream recommendation engines feel like the 90s.
Accuracy comparison based on average f1-scores on public datasets, including the popular Movie Lens dataset, and in production environments.
Occam's recommendation engine
Instead of clunky, and cycle-intensive algorithms, Tamber focuses on the metrics that matter and uses lightweight, heavily optimized models. It's single-minded design that does what it says on the tin - and it’s so fast it runs in 100% real time. Never make a user wait for recommendations to generate. Forget the neolithic practice of batch processing. Stop giving users yesterday’s content.
The popularity feedback loop sucked in high school. It still does now.
Search only works when users already know what they want, while mainstream personalization techniques like Alternating Least Squares can only recommend what's already popular. This results in obvious and robotic suggestions, and creates a popularity feedback loop that amplifies the problem.
Sift out the hidden gems
Tamber solves this underlying issue by learning to understand crowd behavior. Accurately predicting the broader movements of groups (and the niches and sub-cultures they represent) allows Tamber to see past the popularity feedback loop that sinks mainstream techniques. This bigger picture means users get highly accurate recommendations that don't incorrectly favor the already popular, or the old news.
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Want to try our demoware? Our demos are exclusively customized for your needs, so you can experience the accuracy and speed of Tamber first hand.