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Related Questions
- Can you provide an example of how attention weights in a recommendation system can be biased towards certain demographics, such as age or income level?
- How can attention weights perpetuate implicit biases in user behavior, such as over-recommending products to certain groups?
- What are some common pitfalls in designing attention mechanisms that can lead to biased recommendation outcomes?
- Can you explain how attention weights can amplify existing biases in user behavior, such as stereotyping or prejudice?
- How can biased attention weights affect the fairness and diversity of recommendation results, and what are some strategies to mitigate these effects?
- Can you provide an example of how attention weights can be influenced by user behavior, such as click-through rates or engagement metrics?
- How can attention weights be designed to be more transparent and explainable, reducing the risk of perpetuating biases in recommendation systems?
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