A Netflix machine learning algorithm adjusts content recommendations using a decay factor of 0.85 per week. If a show starts with 10,000 views in week 1, how many views are expected in week 5, assuming weekly decay?

["How Netflix’s Machine Learning Algorithm Adjusts Content Recommendations with a 0.85 Weekly Decay Factor", "Netflix’s recommendation engine is a masterpiece of machine learning, continuously tailoring content suggestions to keep users engaged and coming back week after week. One key technical feature is the weekly decay factor, currently set at 0.85, which models how viewer interest in a show decreases over time if not recently engaged with.", "### What Is a Decay Factor?", "The decay factor reflects decreasing viewer interest: each week, the likelihood a user will watch a show drops by multiplying the previous week’s engagement by 0.85. This realistic decay mimics natural viewing habits—viewers may explore a hit initially, but interest naturally wanes unless reignited by timely recommendations.", "### Applying the Decay: From Week 1 to Week 5", "Starting with 10,000 views in Week 1, we apply the decay factor multiplicatively for four consecutive weeks (to reach Week 5):", "- Week 2:\n ( 10,000 \ imes 0.85 = 8,500 )\n- Week 3:\n ( 8,500 \ imes 0.85 = 7,225 )\n- Week 4:\n ( 7,225 \ imes 0.85 = 6,141.25 )\n- Week 5:\n ( 6,141.25 \ imes 0.85 = 5,220.06 )", "So, approximately 5,220 views are expected in Week 5—based on the steady decay without new content exposure or re-engagement.", "### Why This Matters for Netflix’s Algorithm", "The decay factor ensures that recommendations remain dynamic and cognizant of diminishing interest. Instead of perpetually pushing older content, Netflix prioritizes timely, relevant shows while leveraging viewer behavior patterns to fine-tune algorithms. This helps maintain high engagement, reduce platform fatigue, and promote diverse content discovery.", "### Summary", "- Starting views: 10,000 (Week 1)\n- Weekly decay factor: 0.85\n- Expected views in Week 5: 5,220 (rounded)\n- Purpose: Model realistic viewer interest decay to improve personalized recommendations and user retention", "This precise, data-driven approach enables Netflix to deliver fresh, engaging content at just the right moment—keeping every viewer’s journey dynamic and satisfying.", "---", "Keywords: Netflix recommendation algorithm, decay factor 0.85, weekly decay, machine learning content personalization, showing views decay in weeks, viewer engagement model\nAlso search for: how Netflix recommendations use decay factors, machine learning in streaming, personalized content decay rates"]