🤗 Machine learning Engineer blog challenge (2/100) Doordash — 2022 Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers 🤔 What problem do they solve? 他們想要找到一個model去決定要對哪些dasher(運送員)發訊息去送貨,特別在尖峰時刻,supply 不夠多時候,如下圖所示

🤗 Machine learning Engineer blog challenge (1/100) Doordash — 2021 Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings (doordash.engineering) 🤔 What problem do they solve? 他們想要建構出 catalog(品項) embedding 首先什麼是catalog in Doordash (可能有誤解)? 同一家餐廳有很多的品項, 像是salmon sushi, boba tea, 而不同餐廳也可以share同個品項,每一家珍奶店都有個東西叫 “boba tea” 讓boba tea 有自己的representation, 不同家的boba tea share 一樣的representation -> 裡面blog可能沒明說(也許我看漏) 但我從他們後來用品項去建構store embedding,應該是考慮這狀況,而且這樣更可以solve cold-start issue (new store)

🤗 Recommendation system paper challenge (31/50) paper link Google KDD 2021 🤔 What problem do they solve? 現有的MTL (multi-task learning) 沒有針對fairness進行優化,只有STL (single task learning)針對fairness進行優化 所以這篇paper在於解決 fairness on MTL的問題 😎 Contribution 定義新的metric: ARFG: 對每個task去計算MTL中的FPRGap跟STL的FPRGap的比值,在平均起來 ARE: 對每個task去計算MTL中的error rate跟STL的error rate的比值,在平均起來

🤗 Recommendation system paper challenge (30/50) paper link Google KDD 2021

🤗 Recommendation system paper challenge (29/50) paper link 🤔 What problem do they solve? Large Scale item recommendation Given a query x, we would like to recommend items y from M items and we can observe reward (watch time) for each pair (x, y) Label: reward r (watch time) Train data: a pair (query x, item y, reward r) Model: two-tower model…