Wide & Deep Learning for Recommender Systems

24 Jun 2016Heng-Tze ChengLevent KocJeremiah HarmsenTal ShakedTushar ChandraHrishi AradhyeGlen AndersonGreg CorradoWei ChaiMustafa IspirRohan AnilZakaria HaqueLichan HongVihan JainXiaobing LiuHemal Shah

Generalized linear models with nonlinear feature transformations are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Click-Through Rate Prediction Amazon Wide & Deep AUC 0.8637 # 5
Click-Through Rate Prediction Bing News Wide & Deep AUC 0.8377 # 2
Log Loss 0.2668 # 2
Click-Through Rate Prediction Company* Wide & Deep (FM & DNN) AUC 0.8661 # 6
Log Loss 0.02640 # 6
Click-Through Rate Prediction Company* Wide & Deep (LR & DNN) AUC 0.8673 # 3
Log Loss 0.02634 # 3
Click-Through Rate Prediction Criteo Wide & Deep (LR & DNN) AUC 0.7981 # 12
Log Loss 0.46772 # 14
Click-Through Rate Prediction Criteo Wide & Deep (FM & DNN) AUC 0.7850 # 15
Log Loss 0.45382 # 12
Click-Through Rate Prediction Dianping Wide & Deep AUC 0.8361 # 4
Log Loss 0.3364 # 3
Click-Through Rate Prediction MovieLens 20M Wide & Deep AUC 0.7304 # 7

Methods used in the Paper


METHOD TYPE
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