Recommendation Systems

Retrieval

Ranking all of the billions of pieces of content uploaded to Instagram for every single user is obviously not feasible.

Therefore, the candidate generation stage uses a set of heuristics and ML models to narrow down the potential items to thousands of photos/videos.

In terms of heuristics, Instagram uses things like

• Accounts you follow

... See more

What's New (October, 2023)

We are pleased to announce that this repository (formerly known as Microsoft Recommenders, https://github.com/microsoft/recommenders), has joined the Linux Foundation of AI and Data (LF AI & Data)! The new organization, recommenders-team , reflects this change.

We hope this move makes it easy for anyone to contribute! Our... See more

Second Stage

Here, Instagram uses a Multi-Task Multi Label (MTML) neural network model. As the name suggests, this is an ML model that is designed to handle multiple tasks (objectives) and predict multiple labels (outcomes) simultaneously.

For recommendation systems, this means predicting different types of user engagement with a piece of content... See more

First Stage Ranking

After candidates are retrieved, the system needs to rank them by value to the user. This “value” is determined by how likely a user is to engage with the photo/video. Engagement is measured by whether the user likes/comments on it, shares it, watches it fully, etc.

The first stage ranker takes in thousands of candidates from the... See more

With a Two Tower Model, you generate embedding vectors for the user and for all the content you need to retrieve/rank. An embedding vector is just a compact representation that captures the attributes and relationships of an item in a machine-learning-friendly vector.

Once you have these embedding vectors, you can look at the similarity between a... See more

Causal Ranker: A Causal Adaptation Framework for Recommendation Models

Jeong-Yoon Lee, Sudeep Das

Most machine learning algorithms used in personalization and search, including deep learning algorithms, are purely associative. They learn from the correlations between features and outcomes how to best predict a target.

In many scenarios, going beyond... See more

All the recommendation systems you see at Twitter, Facebook, TikTok, YouTube, etc. have a similar high-level architecture.

They have a layered architecture that looks something like the following

1. Retrieval - Narrow down the candidates of what to show a user to thousands of potential items


2. First Stage Ranking - Apply a low-level ranking system to

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The RDRec framework has two main stages:

1. Interaction Rationale Distillation : This step involves extracting detailed user preferences and item attributes from textual reviews. Using a prompt-based approach, the larger LM (like Llama-2-7b) processes these reviews to distinguish between the underlying reasons behind user interactions and item

... See more