[Asis-l] CfP: Special Issue on De-Personalisation, Diversification, Filter Bubbles and Search (IR Journal)

[Ralf B]

CfP: Special Issue on De-Personalisation, Diversification, Filter
Bubbles and Search (IR Journal)
Call for papers for the Information Retrieval Journal
(http://www.springer.com/10791)
Apologies for cross-posting.

Link to the call:
http://static.springer.com/sgw/documents/1631720/application/pdf/CfP_SI_De_Personalisation%2C+Diversification%2C+Filter+Bubbles+and+Search.pdf

Introduction

Information retrieval, recommender systems and, more generally,
approaches in machine learning have resulted in highly personalised
web experiences. Building on context, location and users’ virtual
(social) profiles, the web is highly aligned to users’ perceived
interests, to the interests of ‘similar’ users, and to the interests
of users to whom a user is digitally connected. Whilst this delivers
relevant content, it also polarises informational perspectives and
removes serendipity through the development of filter bubbles or echo
chambers: scenarios where specific ideas, beliefs or data are
reinforced through repetition of a closed system that limits the free
movement of alternative (competing) ideas. There is the implication
that certain ideas or outcomes dominate due to, and resulting in, a
bias concerning how specific input is gathered. Search diversification
has gained significant attention in information retrieval in recent
years as one approach to relax over-focused views on search results
and content. However, methods, reviews and evaluations that aim to
qualify and quantify personalised experiences and their biasing
effects are under-addressed in the literature.

Currently, there is no single source that integrates multidisciplinary
research that conceptualises and evaluates the bias that results from
continuous filtering and personalisation. We aim to address this gap
by accepting a selective set of papers that allows researchers to
better understand the influence that personalisation has on
information experiences. In this context, we aim to bring together a
wide range of views and approaches from information retrieval,
information science, cognitive systems, computational social science
and machine learning.

Topics of Interest

We envisage the following topical categories for submission with a
particular emphasis on variety and cross-disciplinary approaches:

* Reviews: Review papers concerning pertinent aspects of filter
bubbles including understanding and determining the needs and
boundaries of (de-)personalisation;
* Theoretical & Empirical Models: Formal approaches to represent
highly personalised filter bubbles to facilitate experimental
approaches, enable user comprehension, and simulate filter bubbles;
* Metastudies: Studies that attempt to qualify/quantify/visualise the
divergence of users' personalised search results and information
experience(s);
* Experimental Methods: Methodologies for the reproducibility of
studies seeking to investigate filter bubbles;
* Experimental Infrastructures: Systems that help control and compare
the effects of various degrees of (de-)personalised search scenarios;
* IR Experiments: Experiments that demonstrate and formalise any
effects of filtered information experiences;
* Test Collections and Corpora: Practice and experience using,
adapting, merging and/or gathering (test) collections and experimental
data sets;
* User Studies: Studies that consider multiple users or multiple user
profiles (search engines, social media, etc.) and contexts (location,
tasks, devices, etc.) that shed light on the differences in users'
diverging search results and information experience(s), and
* Case Studies: Studies into filter bubbles and discussion on the
tangible effects and observations of (de-)personalisation.

Special Issue Guest Editors

Ralf Bierig (Maynooth University, Ireland) (Contact Person: ralf [dot]
bierig [ad] mu [dot] ie.)
Simon Caton (National College of Ireland)

Important Dates

Initial submission due: July 01, 2018
Initial reviewer feedback: October 01, 2018
Revised submission due:: November 01, 2018
Final reviews and notification: December 15, 2018

Paper Submission

Papers submitted to this special issue for possible publication must
be original and must not be under consideration for publication in any
other journal or conference. Previously published or accepted
conference papers must contain at least 30% new material to be
considered for the special issue.

All papers are to be submitted by referring to
http://www.springer.com/10791 (submit online). At the beginning of the
submission process in Editorial Manager, under “Article Type”, please
select the appropriate special issue.

All manuscripts must be prepared according to the journal publication
guidelines which can also be found on the website provided above.
Papers will be evaluated following the journal's standard review
process.

Contact

For inquiries on the above please contact Ralf Bierig, ralf [dot]
bierig [ad] mu [dot] ie.