Deep Neural Networks for YouTube Recommendations

2016 • Machine Learning • Recommender Systems • ACM • Engineering • Latency • ML • Ranking • Recommender • Scale • Systems

22 Mar 2021

Introduction

• The paper describes YouTube’s deep learning-based recommendation system.

• Link to the paper

Challenges

• Scale - Very large number of users and videos.

• Freshness - Very large number of videos uploaded every hour. The recommendation system should take these new videos into account as well.

• Noise - User satisfaction needs to be modeled from noisy implicit feedback signal as the explicit signal is very sparse.

System Overview

• Two neural networks: one for candidate generation and another one for ranking.

• Metrics

  • Offline metrics like precision, recall, ranking loss

  • A/B testing via live experiments

Candidate Generation

• Input: events from a user’s YouTube activity history.

• Output: small subset (hundreds) of videos.

• Approach:

  • Recommendation is modeled as extreme multiclass classification.

  • Predict the video (from a corpus) that a user will watch at a given time.

  • The neural network’s task is to learn useful user embeddings, given the user’s context and history.

  • For each positive class (relevant video), negative classes (non-relevant videos) are sampled from the video corpus.

• Model Architecture

  • A feedforward network with input as user embeddings and context embeddings (watch history).

  • Watch history is a variable-length sequence of video ids, where each video id is mapped to an embedding.

  • The sequence of video ids is mapped to a sequence of embeddings, and this sequence is averaged to obtain fixed-sized embedding.

  • Additional signals like demographic features and search query embeddings can be added along with the context embeddings.

  • The age of a video is also used as a feature during training to account for the freshness of the content. This feature is set to zero (or slightly negative) during inference.

• Other Insights

  • Training examples are generated from all YouTube watches, including the watches from the videos embedded on other sites, to surface new content.

  • Generating the same number of training examples per user is important to avoid a small set of active users from dominating the model training.

  • Predicting a user’s next watch leads to better results than predicting a randomly held-out watch. This can be attributed to the general consumption pattern of videos (e.g., episodes are usually watched in order).

Ranking

• Input: list of candidate videos to rank from.

• Output: score for each video.

• Approach

  • A feedforward network (similar to candidate generation model) trained using logistic regression loss.

• Feature representation

  • Different types of features: categorical vs. continuous, univalent vs. multivalent, describes video vs. describes user or context.

  • Important signals include user’s interaction with the video (or similar videos), which source/channel added the video to the candidate set.

  • Embeddings are shared across features. For example, the representation for a video id remains the same, irrespective of whether it is being used for representing the “video to recommend” or the “last seen video.”

  • Feature normalization and transformations like exponents (square or square root) for continuous variables improve the performance.

• To model the expected watch time, the logistic regression loss is weighted by the observed watch time. For example, if a video was watched, its weight is given by the observed watch time, and if the video was not watched, its weight is set to 1.

• In practice, this means that the logistic regression model learns odds that approximate the expected watch time of the video.