Naver Search: Deep Learning Powered Search Portal for Intelligent Information Provision

Inho Kang

Abstract: Naver has been the most popular search engine for over a decade in South Korea. As a search portal, Naver aims to match a user's search intentions to the information from the web pages and databases, and to connect users based on shared interests to provide the best way to find the information. Over the past decade, Naver has been trying to better understand Korean users, queries, and web pages for PC and mobile search. In 2002, Naver introduced Knowledge-IN, which was the forerunner of community Question Answering to find out the need of users and topic experts. Users can ask their specific inquiry to appropriate topic experts in their search results. In addition to PC and mobile, Naver is trying to enable a user to access the relevant information using any other device or interface. In detecting common interest groups and good creators, Naver adds device and interface factors. Not only the contents, but also the delivery media types are important in satisfying users on various devices. Deep learning (DL) based methods have tremendous progress in image and text classification. With DL based methods, not only queries, and text documents, but also images, videos, live-streams, locations, etc. are classified and linked to detect common interest groups, and select and rank good creators and good delivery types in each group. With DL, Naver seeks to provide search results that meet user needs more precisely while learning and improving on the fly. In this talk, I'll cover some efforts and challenges in understanding and satisfying users on various devices.