August 20, 2018

Facebook Research at KDD 2018

By: Meta Research

Experts in the research fields of data science, data mining, knowledge discovery, large-scale data analytics and big data are gathering in London, UK this week for the 24th annual conference on Knowledge Discovery and Data Mining (KDD) to present the latest interdisciplinary advances in these fields. Research from Facebook will be presented in oral paper and poster sessions. Facebook researchers and engineers will also be organizing and participating in workshops throughout the week.

Facebook research being presented at KDD 2018

A real-time framework for detecting efficiency regressions in a globally distributed codebase
Martin Valdez-Vivas, Caner Gocmen, AndriiKorotkov, Ethan Fang, Kapil Goenka and Sherry Chen

Multiple teams at Facebook are tasked with monitoring compute and memory utilization metrics that are important for managing the efficiency of the codebase. An efficiency regression is characterized by instances where the CPU utilization or query per second (QPS) patterns of a function or endpoint experience an unexpected increase over its prior baseline. If the code changes responsible for these regressions get propagated to Facebook’s fleet of web servers, the impact of the inefficient code will get compounded over billions of executions per day, carrying potential ramifications to Facebook’s scaling efforts and the quality of the user experience. With a codebase ingesting in excess of 1,000 diffs across multiple pushes per day, it is important to have a real-time solution for detecting regressions that is not only scalable and high in recall, but also highly precise in order to avoid overrunning the remediation queue with thousands of false positives. This paper describes the end-to-end regression detection system designed and used at Facebook. The main detection algorithm is based on sequential statistics supplemented by signal processing transformations, and the performance of the algorithm was assessed with a mixture of online and offline tests across different use cases. We compare the performance of our algorithm against a simple benchmark as well as a commercial anomaly detection software solution.

Rosetta: Large scale system for text localization and recognition in images
Fedor Borisyuk, Albert Gordo and Viswanath Sivakumar

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta’s system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.

TaxoGen: Constructing Topical Concept Taxonomy by Adaptive Term Embedding and Clustering
Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni and Jiawei Han

Taxonomy construction is not only a fundamental task for semantic analysis of text corpora, but also an important step for applications such as information filtering, recommendation, and Web search. Existing pattern-based methods extract hypernym-hyponym term pairs and then organize these pairs into a taxonomy. However, by considering each term as an independent concept node, they over-look the topical proximity and the semantic correlations among terms. In this paper, we propose a method for constructing topic taxonomies, wherein every node represents a conceptual topic and is defined as a cluster of semantically coherent concept terms. Our method, TaxoGen, uses term embeddings and hierarchical cluster-ing to construct a topic taxonomy in a recursive fashion. To ensure the quality of the recursive process, it consists of: (1) an adaptive spherical clustering module for allocating terms to proper levels when splitting a coarse topic into fine-grained ones; (2) a local embedding module for learning term embeddings that maintain strong discriminative power at different levels of the taxonomy. Our experiments on two real datasets demonstrate the effectiveness of TaxoGen compared with baseline methods.

Other activities at KDD 2018

Deep Learning Day
Tutorial: Software for Deep Learning: an overview of PyTorch and others
Soumith Chintala, speaker

Opinions, Conflict, and Abuse in a Networked Society (OCeANS) workshop
Paper: Semisupervised Learning on Heterogeneous Graphs and its Applications to Facebook News Feed
Cheng Ju, James Li, Bram Wasti and Shengbo Guo