What is the effect of recency bias in machine learning filters?
Recency bias in filtering means recent behavior matters most:
Machine learning models often weight recent data more heavily. Your last few sends matter more than sends from six months ago.
Implications:
Recent problems can quickly damage placement, even with strong historical reputation.
Recent improvements can lift placement faster than purely historical approaches would suggest.
Consistency matters. Sustained good behavior compounds positively. Sustained poor behavior compounds negatively.
Your recent voyages matter more than your distant past. Today's behavior shapes tomorrow's welcome.
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