Wide and Deep learning for Recommender Systems
Lets discuss this widely cited Google paper on Wide and Deep learning . The paper mentions that an important challenge in recommender systems is to achieve both memorization and generalization . Memorization is learning the frequent co-occurrence of items and features whereas generalization explores new feature combinations that have rarely occurred in the past. LR models have been widely used in Google settings and generally have sparse features with one hot encoding. Memorization and generalization can be added in such models by cross-product transformations in the feature space but require a lot of manual feature engineering. On the other hand are embedding based models that learn a low dimensional embedding for each of the categorical features. One of the problems with embedding based model is that it will lead to non-zero predictions even when the user-item matrix is high rank and consists of niche users. To solve this problem the authors present a very neat idea - use both wide