Month: April 2019

Grab 的「How we harnessed the wisdom of crowds to improve restaurant location accuracy」這篇是他們的 data team 整理出來，如何使用既有的資料快速的修正餐廳資訊。裡面提到的方法不需要用到 machine learning，光是一些簡單的統計算法就可以快速修正現有的架構。

這些資訊其實是透過司機用的 driver app 蒐集來的，在 driver app 上有大量的資訊傳回伺服器 (像是定時回報的 GPS 位置，以及取餐狀態)，而這些司機因為地緣關係，腦袋裡的資訊比地圖會準不少：

One of the biggest advantages we have is the huge driver-partner fleet we have on the ground in cities across Southeast Asia. They know the roads and cities like the back of their hand, and they are resourceful. As a result, they are often able to find the restaurants and complete orders even if the location was registered incorrectly.

所以透過這些資訊他們就可以反過來改善地圖資料，像是透過司機按下「取餐」的按鈕的地點與待的時間，就可以估算餐聽可能的位置，然後拿這個資訊比對地圖上的資料，就很容易發現搬家但是地圖上沒更新的情況：

Fraction of the orders where the pick-up location was not “at” the restaurant: This fraction indicates the number of orders with a pick-up location not near the registered restaurant location (with near being defined both spatially and temporally as above). A higher value indicates a higher likelihood of the restaurant not being in the registered location subject to order volume

Median distance between registered and estimated locations: This factor is used to rank restaurants by a notion of “importance”. A restaurant which is just outside the fixed radius from above can be addressed after another restaurant which is a kilometer away.

另外也有不少其他的改善 (像是必須在離餐聽某個距離內才能點「取餐」，這個「距離」會因為餐聽可能在室內商場而需要的調整)，整個成果就會反應在訂單的取消率大幅下降：

整體看起來是系統產生清單後讓人工後續處理 (像是打電話去店家問？)，但這個方式所提供的清單準確度應該很高 (因為司機不會沒事跟自己時間過不去，跑到奇怪地方按下取餐)，用這些資料跑簡單的演算法就能夠快速修正不少問題...