BIRDS - Bridging the Gap between Information Science, Information Retrieval and Data Science

The BIRDS workshop aimed to foster the cross-fertilization of Information Science (IS), Information Retrieval (IR) and Data Science (DS). Recognising the commonalities and differences between these communities, the proposed full-day workshop brought together experts and researchers in IS, IR and DS to discuss how they can learn from each other to provide more user-driven data and infor- mation exploration and retrieval solutions. Therefore, the papers aimed to convey ideas on how to utilise, for instance, IS concepts and theories in DS and IR or DS approaches to support users in data and information exploration.

[1]  Massimo Melucci,et al.  An Efficient Algorithm to Compute a Quantum Probability Space , 2019, IEEE Transactions on Knowledge and Data Engineering.

[2]  Haiming Liu,et al.  Theory-Based User Modeling for Personalized interactive Information Retrieval , 2016, UMAP.

[3]  Massimo Melucci.  Evaluation of Information Retrieval Systems Using Structural Equation Modelling , 2019, Comput. Sci. Rev..

[4]  Amit Kumar Jaiswal,et al.  Information Foraging for Enhancing Implicit Feedback in Content-based Image Recommendation , 2019, FIRE.

[5]  Massimo Melucci.  Passage Retrieval: Aprobabilistic Technique , 1998, Inf. Process. Manag..

[6]  Ingo Frommholz,et al.  Cluster-based polyrepresentation as science modelling approach for information retrieval , 2014, Scientometrics.

[7]  Nicola Ferro,et al.  A probabilistic model for stemmer generation , 2005, Inf. Process. Manag..

[8]  Ingo Frommholz,et al.  Applying cross-cultural theory to understand users’ preferences on interactive information retrieval platform design , 2014 .

[9]  Thomas Roelleke,et al.  Scalable DB+IR Technology: Processing Probabilistic Datalog with HySpirit , 2015, Datenbank-Spektrum.

[10]  Amit Kumar Jaiswal,et al.  Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion , 2020, ECIR.

[11]  Paul Mulholland,et al.  Applying information foraging theory to understand user interaction with content-based image retrieval , 2010, IIiX.

[12]  Victoria S. Uren,et al.  A Four-Factor User Interaction Model for Content-Based Image Retrieval , 2009, ICTIR.

[13]  Massimo Melucci,et al.  A characterization of sample selection bias in system evaluation and the case of information retrieval , 2018, International Journal of Data Science and Analytics.