DeepFM: A Factorization-Machine based Neural Network for CTR Prediction

13 Mar 2017Huifeng GuoRuiming TangYunming YeZhenguo LiXiuqiang He

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems. Despite great progress, existing methods seem to have a strong bias towards low- or high-order interactions, or require expertise feature engineering... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Click-Through Rate Prediction Amazon DeepFM AUC 0.8683 # 3
Click-Through Rate Prediction Bing News DeepFM AUC 0.8376 # 3
Log Loss 0.2671 # 3
Click-Through Rate Prediction Company* DeepFM AUC 0.8715 # 1
Log Loss 0.02618 # 1
Click-Through Rate Prediction Criteo DeepFM AUC 0.8007 # 9
Log Loss 0.45083 # 8
Click-Through Rate Prediction Dianping DeepFM AUC 0.8481 # 2
Log Loss 0.3333 # 2
Click-Through Rate Prediction MovieLens 20M DeepFM AUC 0.7324 # 5

Methods used in the Paper


METHOD TYPE
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