You're facing conflicting feedback on algorithm updates. How do you determine the right direction to take?
When your algorithm updates receive mixed reviews, it's crucial to sift through the noise and find a clear path forward. Consider these strategies:
- Weigh the feedback against your data. Look for patterns that support or refute the responses you've received.
- Identify the expertise level of your sources. Feedback from seasoned professionals may carry more weight.
- Test changes in a controlled environment. Implement updates on a small scale first to gauge impact without widespread disruption.
What strategies have worked for you when deciding on algorithm updates?
You're facing conflicting feedback on algorithm updates. How do you determine the right direction to take?
When your algorithm updates receive mixed reviews, it's crucial to sift through the noise and find a clear path forward. Consider these strategies:
- Weigh the feedback against your data. Look for patterns that support or refute the responses you've received.
- Identify the expertise level of your sources. Feedback from seasoned professionals may carry more weight.
- Test changes in a controlled environment. Implement updates on a small scale first to gauge impact without widespread disruption.
What strategies have worked for you when deciding on algorithm updates?
-
I would first seek to understand the context and circumstances of each feedback provider. This allows me to evaluate why the feedback makes sense from their perspective and compare it against the conditions influencing others who offered differing viewpoints. By analyzing these contexts, I can make a more informed decision that aligns with the broader goals and needs of the project.
-
- Crie categorias e classifique os feedbacks em diferentes segmentos. Isso ajudará a ter mais clareza sobre "quem, onde e quando". Desta forma, terá maior visibilidade sobre a opinião dos usuários e saberá agir com precisão. - Com base nisto, quantifique seus feedbacks e transforme-os em métricas que poderão ajudar o time a trabalhar naquilo que realmente precisa ser melhorado ou ainda dar ênfase a algo que está faltando e precisa ser implementado. - Se o algoritmo é direcionado para um público especializado, busque sempre dar maior ênfase para os seus comentários.
-
There are a few approaches to take to help with the solution: 1. The percentage of impact: negative vs positive feedback. 2. Does one algorithm fit a particular context or scenario? Switch the algorithm based on context 3. Allow user to opt-in / opt-out of the updated algorithm 4. If there is a reason to move forward and the old algorithm cannot or should not be supported for some reason, allow the users or stake holders to get used to the new algorithm, and make updates to iron out any issues in the new algorithm. There is never a one size fits all, so the solution will depend on specifics a lot.
-
To handle conflicting feedback on algorithm updates, I focus on 1. the update's core objectives: what are the requirements we aim to solve and in what priority 2. success metrics 3. segment feedback by source: internal stakeholders, or end-users. Prioritize feedback post evaluating Impact-Confidence-Effort framework and validate changes through controlled experiments like A/B testing.
-
When I face conflicting feedback on algorithm updates, I systematically gather and organize it, categorizing by themes and sources. I analyze the feedback's impact, alignment with goals, and severity. I investigate through data analysis, A/B testing, and user research. I prioritize feedback aligned with my algorithm's goals and having the largest impact, acknowledging that trade-offs may be necessary. I make data-driven decisions, iterate on updates, and remain transparent with users about changes and the reasoning behind them.