---
layout: default
permalink: 'reviews/causal-embeddings-for-recommendation.html'
title: 'Causal Embeddings for Recommendation'
---

# 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:
1. Item-to-item similarity systems, that learn embeddings for items and use a distance metric like cosine similarity to identify similar items
2. User-item sequence embeddings, which tries to predict the next item which the user intends to purchase.

## Method


## Observations


---
layout: default
permalink: 'reviews/causal-embeddings-for-recommendation.html'
title: 'Causal Embeddings for Recommendation'
---

# 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:
1. Item-to-item similarity systems, that learn embeddings for items and use a distance metric like cosine similarity to identify similar items
2. 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.

Add very brief details of the method used and observations
Add very brief details of the method used and observations
