NeuIR

NeuIR (pronounced “New IR”) Group is focused on solving problems at the intersection of Deep Neural Network, Big Data, and Multimedia Information Retrieval. We understand the importance of different forms of data (text, speech, images, sensor data), and strive to achieve the following research goals:

• a) Exploring different deep learning techniques in an effort to advance artificial intelligence in information retrieval;
• (b) Identifying challenges in handling big data and developing best practices to solve these problems;
• (c) Developing newer retrieval models and evaluation metrics specific to conversational information retrieval.

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Information Fostering

What are the problems a searcher encounters during a search process that are not matched with the affordances a search system provides? How can we derive potential problems that are present or could occur in the future while a person is searching for information? How do we map a combination of task, topic, and problems to potential help?  Two of the fundamental problems for information seekers are: not being able to express their needs due to lack of understanding of the task/topic at hand, or the way a resource/system being used works; and not knowing what they do not know. Recommendations made by search systems are often limited to suggesting information objects only, and do not explore the possibilities of recommending a process/strategy, people, or other forms of suggestions.  Information Fostering aims to address these problems at a fundamental level of the underlying search task that an information seeker is engaged in – by understanding the nature of that task, explicating the current and potential problems, and offering help that goes beyond what a typical search system could provide. Information Fostering is an idea of providing proactive suggestions and help to information seekers, thus avoiding potential problems and capturing promising opportunities in searching, before it is too late.

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Exploring Fairness in Information Retrieval Systems

How can we balance the inherent bias found in search engines and achieve a sense of fair representation while effectively maintaining a high degree of utility? We are working on addressing inherent bias found in search engine results, exploring all aspects of search engines. Currently, she has explored several ranking strategies to investigate the relationship between utility and fairness within Google search and proposes an entropy based metric to measure the degree of bias, and to effectively evaluate reranking algorithms.

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