How can you avoid creating filter bubbles with recommender systems?

1 Use multiple criteria

One way to avoid creating filter bubbles with recommender systems is to use multiple criteria to generate recommendations, rather than relying on a single factor such as user ratings, purchase history, or browsing behavior. By using multiple criteria, you can diversify the recommendations and expose users to a wider range of options that might suit their needs, interests, or goals. For example, you can use content-based filtering, which recommends items based on their attributes, such as genre, style, or features, or collaborative filtering, which recommends items based on the preferences of other similar users. You can also combine different types of filtering methods to create hybrid recommender systems that balance relevance and diversity.

3 Provide transparency

A third way to avoid creating filter bubbles with recommender systems is to provide transparency, which is the degree to which users can understand how and why the recommendations are made. Transparency can be achieved by providing explanations, feedback mechanisms, or control options for the recommendations, such as showing the criteria, sources, or logic behind the recommendations, allowing users to rate, review, or report the recommendations, or enabling users to adjust, filter, or customize the recommendations. Transparency can help users trust, evaluate, and accept the recommendations, as well as increase their awareness and responsibility for their choices.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?