Grale: Designing Networks for Graph Learning

How can we find the right graph for semi-supervised learning? In real world applications, the choice of which edges to use for computation is the first step in any graph learning process. Interestingly, there are often many types of similarity available to choose as the edges between nodes, and the choice of edges can drastically affect the performance of downstream semi-supervised learning systems. However, despite the importance of graph design, most of the literature assumes that the graph is static. In this work, we present Grale, a scalable method we have developed to address the problem of graph design for graphs with billions of nodes. Grale operates by fusing together different measures of(potentially weak) similarity to create a graph which exhibits high task-specific homophily between its nodes. Grale is designed for running on large datasets. We have deployed Grale in more than 20 different industrial settings at Google, including datasets which have tens of billions of nodes, and hundreds of trillions of potential edges to score. By employing locality sensitive hashing techniques,we greatly reduce the number of pairs that need to be scored, allowing us to learn a task specific model and build the associated nearest neighbor graph for such datasets in hours, rather than the days or even weeks that might be required otherwise. We illustrate this through a case study where we examine the application of Grale to an abuse classification problem on YouTube with hundreds of million of items. In this application, we find that Grale detects a large number of malicious actors on top of hard-coded rules and content classifiers, increasing the total recall by 89% over those approaches alone.Comment: 10 pages, 6 figures, to be published in KDD'2

Recommender Systems for Grocery Retail - A Machine Learning Approach

Recommender systems are present in our daily activities in different moments, such as when choosing a song to listen to or when doing online shopping. It is an everyday reality for people to have the help of computer systems in order to simplify regular decision activities. Grocery shopping is an essential part of people’s life and a frequent activity. Despite being a common habit, each customer has unique routines, needs and preferences regarding products and brands. This information is valuable for grocery retailers to know their customers better and to improve their marketing and operational activities. This dissertation aims to apply machine learning algorithms to the development of a recommender system capable of preparing personalized grocery shopping lists. The proposed architecture is designed to allow integration with different grocery retailers and support distinct TensorFlow algorithms. The process of extracting information from the dataset as features was explored, as well as the tuning of the model hyperparameters, to obtain better results. The recommendation engine is exposed via a distributed software architecture designed to allow retailers to integrate the recommender system with different existing solutions (e.g., websites or mobile applications). A case study to validate the implemented solution was performed, integrating it with a public dataset provided by Instacart. A comparison study between different machine learning algorithms over the adopted dataset has lead to the choice of the gradient boosted trees algorithm. The solution developed in the case study was compared against two non-machine learning approaches at predicting the last purchase of 360 arbitrary test customers. A pattern miningbased solution and a SQL-based heuristic were used. Different evaluation metrics (namely, the average accuracy, precision, recall, and f1-score) were registered. The way association rules with different strengths were reflected in the predictions of the developed solution was also analyzed. The gradient boosted trees-based implementation from the case study was capable of outperforming the compared solutions as far as evaluation metrics are concerned, and has shown a higher capability of predicting at least one correct item per customer. Also, it became evident that the strictest association rules were frequently found in the recommendations. The adopted solution and algorithm have shown promising results and a remarkable capability to provide meaningful predictions to the different customers, evidencing its capability to add value to grocery retail. Nevertheless, there is still potential for further expansion.Os sistemas de recomendação estão presentes no nosso quotidiano, em momentos como a escolha da música a ouvir ou a preparação de compras online. Estamos acostumados a contar com a ajuda de sistemas computacionais para simplificar tarefas habituais que envolvem decisões. Realizar compras de retalho alimentar é uma parte importante e frequente da nossa vida. Apesar de ser um hábito comum, cada um de nós tem as suas próprias rotinas, necessidades e preferências no que toca a produtos e marcas. Esta informação é valiosa para que os retalhistas alimentares consigam conhecer melhor os seus clientes e melhorar atividades operacionais e de marketing. Esta dissertação tem como objetivo a aplicação de algoritmos de machine learning na criação de um sistema de recomendação capaz de preparar listas de compras personalizadas. A arquitetura proposta é desenhada com o objetivo de permitir a integração com diferentes retalhistas e a utilização de diferentes algoritmos em TensorFlow. O processo de extração de informação na forma de features foi explorado, tal como a afinação dos hiperparâmetros do modelo, para obter melhores resultados. O motor de recomendações é exposto através de uma arquitetura de software distribuída, com o propósito de permitir que os retalhistas alimentares possam integrar este sistema com diferentes soluções existentes (e.g., websites ou aplicações móveis). Foi realizado um caso de estudo para validar a solução implementada, através da integração da solução com os dados públicos disponibilizados pelo retalhista Instacart. Uma comparação entre a aplicação de diferentes algoritmos de machine learning aos dados utilizados, levou à adoção do algoritmo gradient boosted trees. A solução desenvolvida no caso de estudo foi comparada com duas abordagens não baseadas em machine learning para a previsão da última compra de 360 clientes arbitrários. Foi usada uma abordagem baseada em pattern mining e uma abordagem baseada em SQL. Diferentes métricas de avaliação (nomeadamente accuracy, precision, recall e f1-score médios) foram registadas. Foi também analisada a forma como diferentes regras de associação se encontraram refletidas nas recomendações da solução desenvolvida. A implementação baseada em gradient boosted trees do caso de estudo superou as soluções com as quais foi comparada quanto às métricas de avaliação, e mostrou uma maior capacidade de recomendar pelo menos um produto correto por cliente. Verificou-se também que as regras de associação mais fortes estão frequentemente refletidas nas recomendações. A abordagem adotada e o algoritmo aprofundado mostraram resultados promissores e uma capacidade notável de fornecer recomendações úteis aos diferentes clientes, evidenciando a sua aptidão para adicionar valor ao retalho alimentar. Ainda assim, este sistema apresenta um elevado potencial para expansão