When recommending places to visit, systems often suggest popular spots or large venues like shopping malls. This can lead to unfair recommendations, as smaller, less-known places get overlooked. There are two main issues at play here. First, there is a tendency to suggest big, collective points of interest over smaller, individual ones. Second, popular places are often recommended more frequently than lesser-known ones. This is what is known as popularity bias.

This unfairness arises because users might visit multiple places within a larger venue, making it hard to track exactly where they went. This uncertainty leads to a new type of bias called scale bias. Scale bias happens when the recommendation system favors larger venues over smaller ones. Both scale bias and popularity bias can make the recommendations feel unfair. They can also make the system less accurate, as it might not truly reflect what the user wants.

To tackle this problem, a unique approach has been developed. It uses conversational techniques to understand each user's preferences better. By asking personalized questions, the system can reduce scale bias. It also uses a special reward system to make sure the recommendations align with the user's past preferences, addressing popularity bias.

The results show that this approach works well. It reduces both types of bias and improves the accuracy of the recommendations. This is because it takes into account each user's unique preferences, making the suggestions more fair and personalized.

It is important to note that this is not a perfect solution. There are still challenges to overcome, such as making the conversations more natural and handling even more complex user preferences. However, this approach is a step in the right direction. It shows that by understanding and addressing these biases, recommendation systems can become more fair and accurate.

Unmasking Hidden Biases in POI Recommendations

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