AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction

25 Mar 2020Bin LiuChenxu ZhuGuilin LiWeinan ZhangJincai LaiRuiming TangXiuqiang HeZhenguo LiYong Yu

Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Click-Through Rate Prediction Criteo AutoDeepFM(3rd) AUC 0.8010 # 10
Log Loss 0.5405 # 16

Methods used in the Paper


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