Head-scratchingly accurate suggestions as
a service

Tamber is the best way to put recommendations in your app. No big data headaches. No '90s algorithms. No tedious tutorials. Just deliciously tasteful, real time personalization.

Integration easy as π.

Find our client libraries on GitHub.

Step 1: Track events as they happen

Stream user interactions in real time, just like you do for Mixpanel or Segment. Every click, like, and purchase helps Tamber learn to understand your platform.

tamber.event.track({
	user: "user_rlox8k927z7p",
	behavior: "purchase",
	item: "item_83jx4c57r2ru",
}, function(err, result){});
e, info, err := event.Track(&tamber.EventParams{
	User:     "user_rlox8k927z7p",
	Behavior: "purchase",
	Item:     "item_83jx4c57r2ru",
})
$ curl https://api.tamber.com/v1/event/track \
	-u Mu6DUPXdDYe98cv5JIfX: \
	-d user=user_rlox8k927z7p \
	-d behavior=purchase \
	-d item=item_83jx4c57r2ru 
HashMap eventParams = new HashMap();
eventParams.put("user", "user_rlox8k927z7p");
eventParams.put("item", "item_83jx4c57r2ru");
eventParams.put("behavior", "purchase");

JSONObject resp = new JSONObject();
try {
    JSONObject resp = tamber.event.track(eventParams);
} catch (TamberException e) {
    System.out.println(String.format("%s=%s", e.getClass().getName(), e.getMessage()));
}
e = tamber.Event.track(
    user='user_rlox8k927z7p',
    behavior='purchase',
    item='item_83jx4c57r2ru'
)
e = Tamber::Event.track(
  :user => 'user_rlox8k927z7p',
  :behavior => 'purchase'
  :item =>  'item_83jx4c57r2ru',
)

Step 2: Discover recommendations that feel tasteful and informed, not robotic

Pull head-scratchingly tasteful recommendations for users in real time, and start solving discovery in your app.

The new standard in recommendation accuracy.

Tamber makes mainstream recommendation engines feel like the 90s.

Accuracy (f1-score)
Run Time (hours)

Accuracy comparison of f1-scores of engines cross-validated on the popular Movie Lens datasets. Read our white paper for a deeper dive.

The curated 1% of content sees over 80% of all user traffic.

Tamber solves discovery for the remaining 99% of your catalogue – so now your users can get to the things they want, even on the roads less traveled.

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 tools like PredictionIO are tedious to implement and 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 highly personalized recommendations that don't incorrectly favor the already popular, or the old news.

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 recommendations.

Unlock your content. Start pushing user interactions in real time today.