705 research outputs found
Random Graph Generator for Bipartite Networks Modeling
The purpose of this article is to introduce a new iterative algorithm with
properties resembling real life bipartite graphs. The algorithm enables us to
generate wide range of random bigraphs, which features are determined by a set
of parameters.We adapt the advances of last decade in unipartite complex
networks modeling to the bigraph setting. This data structure can be observed
in several situations. However, only a few datasets are freely available to
test the algorithms (e.g. community detection, influential nodes
identification, information retrieval) which operate on such data. Therefore,
artificial datasets are needed to enhance development and testing of the
algorithms. We are particularly interested in applying the generator to the
analysis of recommender systems. Therefore, we focus on two characteristics
that, besides simple statistics, are in our opinion responsible for the
performance of neighborhood based collaborative filtering algorithms. The
features are node degree distribution and local clustering coeficient
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
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
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