Research Challenges in Devising the Next Generation Information Retrieval - Emine Yilmaz
Conference Website: http://saiconference.com/FTC
Abstract: With the introduction of new types of devices in our everyday lives (e.g. smart phones, smart watches, smart glasses, etc.), the interfaces over which IR systems are used are becoming increasingly smaller, which limits the interactions users may have. Searching over devices with such small interfaces is not easy as it requires more effort to type and interact with such systems. Hence, building IR systems that can reduce the interactions needed with the device is highly critical. Design, optimisation, and evaluation of retrieval systems has traditionally focused on identifying and retrieving documents relevant to a query submitted by the user. However, with the new devices over which search engines are used for, effort to find relevant information plays a significant role for user satisfaction. In this talk, I will first argue that effort to find relevant information in a document can have a significant impact on user satisfaction, arguing that more research should be put into devising retrieval methods that aim at minimising user effort, given a query. Ideally, a search engine should be able to understand the reason that caused the user to submit a query and it should help the user achieve the actual task by guiding her through the steps (or subtasks) that need to be completed. Devising such task based information retrieval systems have several challenges that have to be tackled. In the first part of this talk, I will focus on the problems that need to be solved when designing such systems, as well as the progress that we have made in these areas. In the second part of the talk, I will emphasize the importance of detecting misinformation and bias available online and describe some of the work we have done to tackle the issues of misinformation and bias.
Emine Yilmaz is a Professor and Turing Fellow at University College London, Department of Computer Science. She also works as an Amazon Scholar as part of the Amazon Alexa team, where he manages a group of scientists. Her research interests lie in the areas of information retrieval, natural language processing and applications of machine learning. She has served in various senior roles, including co-editor-in-chief for the Information Retrieval Journal, a member of the editorial board for the AI Journal and an elected member of the executive committee for ACM SIGIR. She is the recipient of Karen Sparck Jones 2015 Award for the contributions of her work to information retrieval research. She is also one of the recipients of the Google Faculty Research Award in 2015 and the Bloomberg Data Science Research Award in 2018.