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Build a recommender system with Spark: Logistic Regression – I failed...
source link: https://vinta.ws/code/build-a-recommender-system-with-spark-logistic-regression.html?
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在本篇文章中，我們將以 Ranking 階段常用的方法之一：Logistic Regression 邏輯迴歸為例，利用 Apache Spark 的 Logistic Regression 模型建立一個 GitHub repositories 的推薦系統，以用戶對 repo 的打星紀錄和用戶與 repo 的各項屬性做為特徵，預測出用戶會不會打星某個 repo（分類問題）。最後訓練出來的模型就可以做為我們的推薦系統的 Ranking 模組。不過因為 LR 是線性模型，所以通常需要大量的 Feature Engineering 來習得非線性關係。所以這篇文章的重點是 Spark ML 的 Pipe
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