Ten Mistakes to Avoid When Creating a Recommendation System « Machine Learning Times – The Machine Learning Times

npressfetimg-1580.png
Originally published in Medium.com, 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.

To continue reading this article, click here.

Source: https://www.predictiveanalyticsworld.com/machinelearningtimes/ten-mistakes-to-avoid-when-creating-a-recommendation-system/12718/

Leave a Reply

Your email address will not be published.

npressfetimg-91.png
Machine learning

A far-sighted approach to machine learning | MIT News | Massachusetts Institute of Technology – MIT News

Picture two teams squaring off on a football field. The players can cooperate to achieve an objective, and compete against other players with conflicting interests. That’s how the game works.

Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents…….

Read More
npressfetimg-16.png
Machine learning

A far-sighted approach to machine learning | MIT News | Massachusetts Institute of Technology – MIT News

Picture two teams squaring off on a football field. The players can cooperate to achieve an objective, and compete against other players with conflicting interests. That’s how the game works.

Creating artificial intelligence agents that can learn to compete and cooperate as effectively as humans remains a thorny problem. A key challenge is enabling AI agents…….

Read More
npressfetimg-990.png
Machine learning

2023 Trends in Artificial Intelligence and Machine Learning: Generative AI Unfolds – insideBIGDATA

At present, the potential for generative Artificial Intelligence—the variety of predominantly advanced machine learning that analyzes content to produce strikingly similar new content—is boundless.

These technologies have transcended Natural Language Generation, in which they achieved much of their early renown via paradigms such as Bidirectional Encoder Representations from Tran…….

Read More