Machine Learning Times
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1 week ago
Ten Mistakes to Avoid When Creating a Recommendation System

Originally published in, July 27, 2022. 

We’ve been long working on improving the user experience in UGC products with machine learning. Here are our ten key lessons of implementing recommendation systems in business to build a really good product.

1. Define a Goal that Really Contributes to the Business Tasks

The global task of the recommendation system is to select a shortlist of content from a large catalog that is most suitable for a particular user. The content itself can be different — from products in the online store and articles to banking services. FunCorp product team works with the most interesting kind of content — we recommend memes.

To do this, we rely on the history of the user’s interaction with the service. But “good recommendations” from a user perspective and from a business perspective are not always the same thing. For example, we found that increasing the number of likes that a user clicks thanks to more accurate recommendations does not affect retention, a metric that is important for our business. So we started focusing on models that optimize time spent in the app instead of likes.

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