I'm thinking about the two following approaches for building a recommender system to recommend products using implicit data as a classifier:
- Treat it as a multi-class classification problem. The features of the model are the user features and the target is the item. This is the approach used in this Google documentation.
- Treat it as a binary classification problem. The features of the model are the user and item features, and the target variable is a binary variable indicating whether the user purchased this item. This is the approach used in Tensorflow recommenders.
What are the advantages and disadvantages of using one or the other? Is the first approach implemented in any recommendation systems library?