2 BACKGROUND AND RELATIONSHIPS TO CHIIR Recent years saw an emergence of formal retrieval models that were informed by theories coming from Information Science (IS) as reported in [7], [1], [4], [5], [2], and [6], for example. These IS-informed formal retrieval models try to put the focus on users and their interaction with the retrieval system. However, work in building formal approaches and models that are expressive enough to incorporate user behaviour in the retrieval systems are still in their infancy. One approach to de ne IS-informed formal retrieval models can be suggested by the increasing availability of di erent data sources either commercially or openly that led to the emergence of Data Science (DS). Interestingly, DS is not only about batch processing of the large datasets. Whereas Machine Learning (ML) provides predictions for the data sources with very little user input, Exploratory Data Analysis (EDA) allows the users to interact with the system for nding data and patterns of the data which cannot easily be found through traditional database querying. Similarly, some research areas of Information Retrieval (IR) have extensively and deeply been in uenced and characterized by Statistics and Probability Theory. As a consequence, IR is more and more regarded as part of DS, as evidenced by the recent challenges and activities when it comes to COVID-19 testbeds containing scienti c
[1]
Kirk Roberts,et al.
TREC-COVID: rationale and structure of an information retrieval shared task for COVID-19
,
2020,
J. Am. Medical Informatics Assoc..
[2]
C. J. van Rijsbergen,et al.
Supporting polyrepresentation in a quantum-inspired geometrical retrieval framework
,
2010,
IIiX.
[3]
Amit Kumar Jaiswal,et al.
Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation
,
2019,
FIRE.
[4]
Guido Zuccon,et al.
An Analysis of Theories of Search and Search Behavior
,
2015,
ICTIR.
[5]
Ingo Frommholz,et al.
BIRDS - Bridging the Gap between Information Science, Information Retrieval and Data Science
,
2020,
SIGIR.
[6]
Norbert Fuhr,et al.
A probability ranking principle for interactive information retrieval
,
2008,
Information Retrieval.
[7]
Paul Mulholland,et al.
Applying information foraging theory to understand user interaction with content-based image retrieval
,
2010,
IIiX.