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The Future of AI in Everyday Life



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1. Introducti>3) Query throughput, latency Higher‑order n‑grams reduce ambiguity


Relevance Feedback Feedback type Implicit (clicks), explicit (rating), none MRR, MAP Explicit feedback yields better ranking


Data Source Corpus size 100k docs, 500k docs, 1M docs Retrieval time, accuracy Larger corpus may increase noise but improve recall


Evaluation Metric Metrics used Precision@10, Recall@10, F1, NDCG Comparative ranking of methods Different metrics highlight varying strengths


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7. Concluding Thoughts




The exploration began with an intuitive reliance on keyword matching and evolved into a nuanced understanding of user intent, relevance modeling, and interaction dynamics.


While the theoretical foundation is robust, practical deployment demands careful handling of data quality, computational constraints, and evolving user behaviors.


Future work should consider multimodal search (e.g., image-based queries), personalized ranking based on implicit feedback, and real-time adaptability to emerging trends.






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