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

Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.Comment: 9 pages, 3 figures, updated with additional experimental evaluatio

Graph Neural Network for Service Recommender System in Digital Service Marketplace

The emergence of the platform economy has resulted in the decline of many traditional forms of doing business. Freelance work makes use of a platform to connect businesses or people with other businesses or persons in order to solve particular issues or deliver specific services in return for payment. The pairing process involves a buyer that needs work done, a platform that handles the algorithm, and a worker who is willing to do the job via the platform. This research argues that by efficiently pairing the talents of workers to the requirements of buyers, the platforms have the ability to expedite business operations for buyers, empower platform workers, and significantly improve the overall customer experience. Graph Convolutional Networks (GCNs) are inspired by CNNs and aim to expand the convolution operation from grid records to graph records, which in turn facilitates advances in the graph domain. In order to develop reliable and accurate embeddings for digital service recommendation, we employed a graph-based technique on a freelance platform dataset using the graph linkages of services and buyer data. We employed an aggregation-based inductive graph convolution network, namely, Graph SAmple and aggreGatE (GraphSAGE). It is a generalized inductive architecture that learns to construct embeddings for previously unknown data by sampling and combining attributes from a node's immediate neighborhood. We also applied PinSage, a stochastic Graph Convolutional Network (GCN) that can learn node embeddings in platform networks with many digital services. When a robust recommender system is used in digital service marketplace, it can offer promising results that may increase users' satisfaction with the service and boost the platform's ability to increase revenue

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research