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Related Questions
- Can biased user behavior, such as gaming the system, impact the performance of attention-based recommendation models?
- How can the selection of attention weights, particularly when using learnable attention weights, lead to biased outcomes?
- What are some potential pitfalls in designing attention mechanisms that can result in biased recommendation outcomes, such as neglecting long-tail items or overemphasizing popular items?
- Can the use of pre-trained language models as attention mechanisms introduce biases in the recommendation outcomes?
- How can the lack of diversity in the training data affect the performance of attention-based recommendation models and lead to biased outcomes?
- Can the use of attention mechanisms that rely on user interaction data, such as clicks or purchases, lead to biased outcomes if the data is not representative of the target population?
- What are some potential solutions to mitigate the risks of biased recommendation outcomes in attention-based models, such as using debiasing techniques or incorporating fairness constraints into the model optimization objective?
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