Explainable Information Retrieval: A Survey

Explainable information retrieval is an emerging research area aiming to make transparent and trustworthy information retrieval systems. Given the increasing use of complex machine learning models in search systems, explainability is essential in building and auditing responsible information retrieval models. This survey fills a vital gap in the otherwise topically diverse literature of explainable information retrieval. It categorizes and discusses recent explainability methods developed for different application domains in information retrieval, providing a common framework and unifying perspectives. In addition, it reflects on the common concern of evaluating explanations and highlights open challenges and opportunities.Comment: 35 pages, 10 figures. Under revie

Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Axiomatic Analysis of Unsupervised Diversity on Large-Scale High-dimensional Data

Diversity is a concept widely used in every corner of our society. It represents the "breadth" of a set of objects, which needs to be promoted or reduced in different scenarios. Though many people have discussed it, how to define diversity in a reliable way is still a non-trivial task. In particular, when we are facing large-scale high-dimensional data, it is impossible to use pre-defined classifications to divide each object into categories and utilize diversity measurements in downstream tasks. An unsupervised methodology is necessary to handle this challenge. In this dissertation, I explore different methods to address the research question: how to measure diversity in an unsupervised manner based on large-scale high-dimensional data. I leverage representation learning algorithms to project objects into a discrete or continuous space and design several metrics to measure diversity in real-world applications. Furthermore, I introduce an axiomatic analysis method to help us choose and evaluate diversity metrics in both discrete and continuous settings. Following the guidelines derived from the axiomatic analysis, I define diversity in terms of metrics to map distributions of topics to real numbers in discrete space. I also find a simple and intuitive metric to measure diversity, which is defined in continuous space, that performs surprisingly well to satisfy different axioms. The sound and reliable metrics motivate me to focus on some controversial research topics in real applications. I explore the effect of research diversity i.e., how broad researchers' research interests are. I conduct several studies to figure out whether publishing papers with high diversity results in greater research impact. Furthermore, I track trajectories of researchers' careers and try to find the effects of research diversity at different stages. Another real-world application appears in online social networks. Structural diversity, the closeness of users' friends, has a substantial influence on users' behavior from many perspectives. I define users' structural diversity using the results of axiomatic analysis. I track the pattern within the variation in structural diversity in both static and dynamic networks and simulate it with an intuitive graph generation algorithm. An interesting pattern of structural diversity and user engagement in online social media is illustrated.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169733/1/shiyansi_1.pd