31 research outputs found
Third International Workshop on Gamification for Information Retrieval (GamifIR'16)
Stronger engagement and greater participation is often crucial
to reach a goal or to solve an issue. Issues like the emerging
employee engagement crisis, insufficient knowledge sharing,
and chronic procrastination. In many cases we need and
search for tools to beat procrastination or to change people’s
habits. Gamification is the approach to learn from often fun,
creative and engaging games. In principle, it is about understanding
games and applying game design elements in a
non-gaming environments. This offers possibilities for wide
area improvements. For example more accurate work, better
retention rates and more cost effective solutions by relating
motivations for participating as more intrinsic than conventional
methods. In the context of Information Retrieval (IR)
it is not hard to imagine that many tasks could benefit from
gamification techniques. Besides several manual annotation
tasks of data sets for IR research, user participation is important
in order to gather implicit or even explicit feedback
to feed the algorithms. Gamification, however, comes with
its own challenges and its adoption in IR is still in its infancy.
Given the enormous response to the first and second
GamifIR workshops that were both co-located with ECIR,
and the broad range of topics discussed, we now organized
the third workshop at SIGIR 2016 to address a range of
emerging challenges and opportunities
How can heat maps of indexing vocabularies be utilized for information seeking purposes?
The ability to browse an information space in a structured way by exploiting
similarities and dissimilarities between information objects is crucial for
knowledge discovery. Knowledge maps use visualizations to gain insights into
the structure of large-scale information spaces, but are still far away from
being applicable for searching. The paper proposes a use case for enhancing
search term recommendations by heat map visualizations of co-word
relation-ships taken from indexing vocabulary. By contrasting areas of
different "heat" the user is enabled to indicate mainstream areas of the field
in question more easily.Comment: URL workshop proceedings: http://ceur-ws.org/Vol-1311
Benchmarking news recommendations: the CLEF NewsREEL use case
The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms. The goal is to create an algorithm that is able to generate news items that users would click, respecting a strict time constraint. The lab challenges participants to compete in either a "living lab" (Task 1) or perform an evaluation that replays recorded streams (Task 2). In this report, we discuss the objectives and challenges of the NewsREEL lab, summarize last year's campaign and outline the main research challenges that can be addressed by participating in NewsREEL 2016
Bibliometric-enhanced Information Retrieval: 2nd International BIR Workshop
This workshop brings together experts of communities which often have been
perceived as different once: bibliometrics / scientometrics / informetrics on
the one side and information retrieval on the other. Our motivation as
organizers of the workshop started from the observation that main discourses in
both fields are different, that communities are only partly overlapping and
from the belief that a knowledge transfer would be profitable for both sides.
Bibliometric techniques are not yet widely used to enhance retrieval processes
in digital libraries, although they offer value-added effects for users. On the
other side, more and more information professionals, working in libraries and
archives are confronted with applying bibliometric techniques in their
services. This way knowledge exchange becomes more urgent. The first workshop
set the research agenda, by introducing in each other methods, reporting about
current research problems and brainstorming about common interests. This
follow-up workshop continues the overall communication, but also puts one
problem into the focus. In particular, we will explore how statistical
modelling of scholarship can improve retrieval services for specific
communities, as well as for large, cross-domain collections like Mendeley or
ResearchGate. This second BIR workshop continues to raise awareness of the
missing link between Information Retrieval (IR) and bibliometrics and
contributes to create a common ground for the incorporation of
bibliometric-enhanced services into retrieval at the scholarly search engine
interface.Comment: 4 pages, 37th European Conference on Information Retrieval, BIR
worksho
Editorial for the Bibliometric-enhanced Information Retrieval Workshop at ECIR 2014
This first "Bibliometric-enhanced Information Retrieval" (BIR 2014) workshop
aims to engage with the IR community about possible links to bibliometrics and
scholarly communication. Bibliometric techniques are not yet widely used to
enhance retrieval processes in digital libraries, although they offer
value-added effects for users. In this workshop we will explore how statistical
modelling of scholarship, such as Bradfordizing or network analysis of
co-authorship network, can improve retrieval services for specific communities,
as well as for large, cross-domain collections. This workshop aims to raise
awareness of the missing link between information retrieval (IR) and
bibliometrics / scientometrics and to create a common ground for the
incorporation of bibliometric-enhanced services into retrieval at the digital
library interface. Our interests include information retrieval, information
seeking, science modelling, network analysis, and digital libraries. The goal
is to apply insights from bibliometrics, scientometrics, and informetrics to
concrete practical problems of information retrieval and browsing.Comment: 4 pages, Bibliometric-enhanced Information Retrieval Workshop at ECIR
2014, Amsterdam, N
Overview of LiLAS 2020 -- Living Labs for Academic Search
Academic Search is a timeless challenge that the field of Information
Retrieval has been dealing with for many years. Even today, the search for
academic material is a broad field of research that recently started working on
problems like the COVID-19 pandemic. However, test collections and specialized
data sets like CORD-19 only allow for system-oriented experiments, while the
evaluation of algorithms in real-world environments is only available to
researchers from industry. In LiLAS, we open up two academic search platforms
to allow participating research to evaluate their systems in a Docker-based
research environment. This overview paper describes the motivation,
infrastructure, and two systems LIVIVO and GESIS Search that are part of this
CLEF lab.Comment: Manuscript version of the CLEF 2020 proceedings pape
A Comparative Analysis of Retrievability and PageRank Measures
The accessibility of documents within a collection holds a pivotal role in
Information Retrieval, signifying the ease of locating specific content in a
collection of documents. This accessibility can be achieved via two distinct
avenues. The first is through some retrieval model using a keyword or other
feature-based search, and the other is where a document can be navigated using
links associated with them, if available. Metrics such as PageRank, Hub, and
Authority illuminate the pathways through which documents can be discovered
within the network of content while the concept of Retrievability is used to
quantify the ease with which a document can be found by a retrieval model. In
this paper, we compare these two perspectives, PageRank and retrievability, as
they quantify the importance and discoverability of content in a corpus.
Through empirical experimentation on benchmark datasets, we demonstrate a
subtle similarity between retrievability and PageRank particularly
distinguishable for larger datasets.Comment: Accepted at FIRE 202
Improving Contextual Suggestions using Open Web Domain Knowledge
Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Contextual suggestion aims at recommending items to users given
their current context, such as location-based tourist recommendations.
Our contextual suggestion ranking model consists of two
main components: selecting candidate suggestions and providing a
ranked list of personalized suggestions. We focus on selecting appropriate
suggestions from the ClueWeb12 collection using tourist
domain knowledge inferred from social sites and resources available
on the public Web (Open Web). Specifically, we generate two
candidate subsets retrieved from the ClueWeb12 collection, one by
filtering the content on mentions of the location context, and one
by integrating domain knowledge derived from the OpenWeb. The
impact of these candidate selection methods on contextual suggestion
effectiveness is analyzed using the test collection constructed
for the TREC Contextual Suggestion Track in 2014. Our main findings
are that contextual suggestion performance on the subset created
using OpenWeb domain knowledge is significantly better than
using only geographical information. Second, using a prior probability
estimated from domain knowledge leads to better suggestions
and improves the performance