The whole system consists of different models each suitable for a different situation and it decides automatically based on some parameters which to use the most
This model
Both ALS and DNN have their own pros and cons but in a complementary way. Using them together can be a way of solving popular recommender system challenges.
Preprocessing
data loading and cleaning
drop duplicated records
dealing with missing values and outliers
feature encoding
matrix creation
Candidate generation using WALS
demeanifying the matrix(takes care of sparsity)
creating item and user embedding
Uses user embedding to calculate item embedding and vice versa.
candidate generation solves scalability issue
WALS can be parallelized
Automated Bayesian HP tuning
Candidate ranking using DNN
adding item features
semantic item embeddings are fed to the network as input.
ranking using softmax model
The trained model gives a prediction for every user-item pair to say if the item should be recommended or not.
complex and non-linear feature extraction
“deep and wide” implementation
The theory states that“wide” linear models can effectively
memorize sparse feature interactions using cross-product feature transformations, while“deep” neural networks can generalize to previously unseen feature interactions through low dimensional embeddings.
In practice Google used this technique in Google Play store and saw improvement in users feedback.
Hybrid ALS-DNN Model:
Overview
Challenges and solutions