our great Solution

Cold Start Recommendation

For a first time user of your brand, attempting to provide recommendations without data-sets reflecting interests and tastes can be challenging, this is what we would categorize as a cold-start problem.

We are implementing our deep content extraction combined with cross-channel pattern recognition and innovative machine learning algorithms. These methods combined provide a solution for extracting personalized recommendations with limited data sets on user tastes & preferences.

Product

Recommendation API

Results

Increase Retention
Increase Engagement
Increase User Acquisition

Predicting without Knowing

Because human behavior is not linear, cross-referencing data with traditional suggestion models seen on most sites with recommendation capability will only yield lackluster results at best. The most efficient and effective method of generating recommendations for users with little to no upfront data is using robust embeddings and algorithms trained specifically to solve the cold-start problem when engaging new users.

Interested in trying our Recommendation API?

We are very exited to share our newly released Recommendation API, which businesses can utilize to offer their customers the best recommendations possible.

If you are interested in being among the first to try it out, please answer the interest form. Our team will reach back to you as we are processing the flow of applications. Thanks!

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The Technology Behind

Deploying trained embeddings and algorithms allows us to leverage not only the user-item feedback but the proper content of the items and the noticeable feature of the users simultaneously. These embeddings can be harnessed and used with little to no user feedback.

In addition, by adding layers of collaborative filtering, even more, embeddings are generated by identifying users with similar preferences. This also provides a new & unique solution to the cold-start problem for integrating both new users and new content.