LinkedIn @ KDD 2018
LinkedIn operates the world’s largest professional network with more than 560 million members in over 200 countries and territories. Our unique datasets give our AI experts and data scientists the ability to conduct applied research that fuel LinkedIn’s data driven products (People You May Know, Jobs You May Be Interested In, Feed).
LinkedIn’s team of AI engineers and scientists work with massive datasets, solve real problems for our members around the world, and publish at major conferences. They actively contribute to the open source community and are pursuing research in areas such as: computational advertising, machine learning, scalable AI infrastructure, recommender systems, and more.
Applied Data Science Invited Talk: Building Near Realtime Contextual Recommendations for Active Communities on LinkedIn
Hema Raghavan (LinkedIn)
At LinkedIn our mission is to build active communities for all of our members such that members are able to disseminate or seek professional content at the right time on the right channel. We mine a variety of data sources including LinkedIn's Economic Graph and member activities on the site and use large scale machine learning algorithms to recommend members to connect to people they might know to build active communities. We build real-time recommendations to disseminate information so that members never miss a relevant conversation that is going on in any of the communities they are part of. Through this talk we will showcase how we are trying to solve some of the most challenging problems on internet-scale social network analysis, streaming algorithms, and multi-objective optimization.
Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM’18)
Yongzheng Zhang (LinkedIn), Bing Liu (University of Illinois at Chicago), Erik Cambria (Nanyang Technological University), Xiaodan Zhu (Queen's University)
WISDOM aims to provide an international forum for researchers to share information on their latest investigations in opinion mining and sentiment analysis. The broader context of the workshop comprehends opinion mining, social media marketing, information retrieval, and natural language processing. WISDOM'18 consists of two keynote speeches from top researchers in this field plus peer-reviewed papers with latest developments especially deep learning methods for sentiment analysis.
Tutorial: End-to-end Goal-oriented Questions Answering Systems
Deepak Agarwal, Bee-Chung Chen, Qi He, Mikhail Obukhov, Jaewon Yang, Liang Zhang (LinkedIn)
In this tutorial, we first introduce a variety of QA systems based on knowledge graph and intent classification proposed by pioneer researchers. The audience can easily comprehend what common technical components remain challenging and what unique engineering heuristics are useful. For the first time, the audience can learn in-depth not only the scientific methods that boost the precision of question understanding and answer retrieval/generation, but also our practical experiences as well as engineering designs that enable an end-to-end system. Then, on top of three LinkedIn real scenarios, we share our hands-on experiences in the end-to-end process of building goal oriented bots, including problem analysis from scratch, architecture design, training data collection, paraphrase generation, intent modeling and dialogue management. Our goal is that, after this tutorial, the audience knows how to efficiently build a goal-oriented bot without getting stuck in unrealistic solutions.
Tutorial: Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons Learned
Krishnaram Kenthapadi (LinkedIn), Ilya Mironov (Google), Abhradeep Guha Thakurta (UC Santa Cruz)
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS, Google’s RAPPOR, LinkedIn Salary, and Uber’s SQL differential privacy tool. We will also discuss various open source as well as commercial privacy tools, and conclude with open problems and challenges for KDD community.
Research @ LinkedIn