From the course: Building Recommender Systems with Machine Learning and AI

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Filter bubbles, trust, and outliers

Filter bubbles, trust, and outliers - Python Tutorial

From the course: Building Recommender Systems with Machine Learning and AI

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Filter bubbles, trust, and outliers

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- The next real-world challenge is a tough one. It's called filter bubbles, and it refers to societal problems that arise when all you show people are things that appeal to their existing interests. Let's say you're building a recommender system for a bookstore. If someone buys a book about a topic associated with right-wing politics, your recommender system will probably pick up on that and start recommending more right-wing books to that person. If they respond to those recommendations, that person gets more and more immersed in right-wing ideology. The same happens for someone who bought a book about left-wing politics. The end result is that the recommender systems we develop to try and show people interesting things creates a more polarized society. This isn't something we ever anticipated in the early days of building recommender systems; but as the same techniques have been applied to social networks and advertising, it really has caused people to be exposed almost exclusively…

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