【推荐系统】深度推荐系统总结

1 Google deep & wide app recommender system

 


Figure : Wide & Deep model structure.

1 Input Features

·Deep

Including
continuousand enumerated features. Enumerated features are changed toembedding,
randomly initialized and convergence after deep&wide model’s training, as MLP’s inputs.

For example:

Continuous features such as app installed age arediscretized
by quantile to [0,1].

·Wide

History items
cross product current item to classification. E.g. History items are {A, B, C}, current item is {D}.(A and D) is 1 if user satisfy A and D simultaneously, otherwise 0.

·Compare of Deep and Wide

Deep can find hidden features through relative low dimension features, but has more complex computing.

Wide with elaborately designing high dimension features can reach good result.. It’s simple, scalable, and interpretable.

 

2 Experiment

+3.9%relative to the control group (statistically significant).