3 research outputs found
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.Comment: KDD 201
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Sequential models that encode user activity for next action prediction have
become a popular design choice for building web-scale personalized
recommendation systems. Traditional methods of sequential recommendation either
utilize end-to-end learning on realtime user actions, or learn user
representations separately in an offline batch-generated manner. This paper (1)
presents Pinterest's ranking architecture for Homefeed, our personalized
recommendation product and the largest engagement surface; (2) proposes
TransAct, a sequential model that extracts users' short-term preferences from
their realtime activities; (3) describes our hybrid approach to ranking, which
combines end-to-end sequential modeling via TransAct with batch-generated user
embeddings. The hybrid approach allows us to combine the advantages of
responsiveness from learning directly on realtime user activity with the
cost-effectiveness of batch user representations learned over a longer time
period. We describe the results of ablation studies, the challenges we faced
during productionization, and the outcome of an online A/B experiment, which
validates the effectiveness of our hybrid ranking model. We further demonstrate
the effectiveness of TransAct on other surfaces such as contextual
recommendations and search. Our model has been deployed to production in
Homefeed, Related Pins, Notifications, and Search at Pinterest.Comment: \c{opyright} {ACM} {2023}. This is the author's version of the work.
It is posted here for your personal use. Not for redistribution. The
definitive Version of Record was published in KDD'23,
http://dx.doi.org/10.1145/3580305.359991