Causal Embeddings for Recommendation
Idea
The authors argue that recommendation systems that optimize for time-spent are indirect optimization methods compared to approaches that just predict items based on a user’s past. They learn a recommendation policy that tries to infer the desired outcome from organic user behavior.
The idea presented is a riff on simple matrix factorization methods and uses the outcome of randomized recommendations’ outcomes to create user and item representations.
Background
Previous approaches to item recommendations are broadly classified into 2 categories:
- Item-to-item similarity systems, that learn embeddings for items and use a distance metric like cosine similarity to identify similar items
- User-item sequence embeddings, which tries to predict the next item which the user intends to purchase.
Method
- The authors attempt to jointly factorize the matrix of control observations and the matrix of treatment observations.
- They utilize an algorithm the call the ‘CausE’ algorithm, that generates the recommendations.
Observations
- The proposed algorithms outperformed the baselines compared against for the MovieLens and Netflix datasets.