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Pinterest "Pinnability" Machine Learning For Home Feed Relevance
In order to accurately predict how likely a Pinner will interact with a Pin, we applied state-of-the-art machine learning models including Logistic Regression (LR), Support Vector Machines (SVM), Gradient Boosted Decision Trees (GBDT) and Convolutional Neural Networks (CNN). We extracted and tested thousands of textual and visual features that are useful for accurate prediction of the relevance score. Before we launch a model for an online A/B experiment, we thoroughly evaluate its offline performance based on historical data. Pinterest "Pinability" Machine Learning For Home Feed Relevance [engineering.pinterest.com]
In training Pinnability models, we use Area Under the ROC Curve (AUC) as our main offline evaluation metric, along with r-square and root mean squared error. We optimized for AUC not only because it is a widely used metric in similar prediction systems, but also because we’ve observed strong positive correlation between the AUC gain from offline testing and an increase in Pinner engagement in online A/B experiment. Our p
In order to accurately predict how likely a Pinner will interact with a Pin.
Why do we promote these scrapers by talking about them, again, and again? They outrank our own work in SERP, yet we give them a word of mentioned?
