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Applied Machine Learning For Search Engine Relevance
The document discusses machine learning techniques for search engine relevance and personalized recommendations. It describes using linear regression models to predict relevance scores and regularization methods like Tikhonov regularization to avoid overfitting. It also discusses using empirical Bayesian models like the Poisson gamma model to estimate relevance probabilities. Finally, it mentions using techniques like singular value decomposition and non-negative matrix factorization to find patterns in user behavior data and remove noise.
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