438 research outputs found
Understanding Mobile Search Task Relevance and User Behaviour in Context
Improvements in mobile technologies have led to a dramatic change in how and
when people access and use information, and is having a profound impact on how
users address their daily information needs. Smart phones are rapidly becoming
our main method of accessing information and are frequently used to perform
`on-the-go' search tasks. As research into information retrieval continues to
evolve, evaluating search behaviour in context is relatively new. Previous
research has studied the effects of context through either self-reported diary
studies or quantitative log analysis; however, neither approach is able to
accurately capture context of use at the time of searching. In this study, we
aim to gain a better understanding of task relevance and search behaviour via a
task-based user study (n=31) employing a bespoke Android app. The app allowed
us to accurately capture the user's context when completing tasks at different
times of the day over the period of a week. Through analysis of the collected
data, we gain a better understanding of how using smart phones on the go
impacts search behaviour, search performance and task relevance and whether or
not the actual context is an important factor.Comment: To appear in CHIIR 2019 in Glasgow, U
BroDyn’18: Workshop on analysis of broad dynamic topics over social media
This book constitutes the refereed proceedings of the 40th European Conference on IR Research, ECIR 2018, held in Grenoble, France, in March 2018.
The 39 full papers and 39 short papers presented together with 6 demos, 5 workshops and 3 tutorials, were carefully reviewed and selected from 303 submissions. Accepted papers cover the state of the art in information retrieval including topics such as: topic modeling, deep learning, evaluation, user behavior, document representation, recommendation systems, retrieval methods, learning and classication, and micro-blogs
Active Sampling for Large-scale Information Retrieval Evaluation
Evaluation is crucial in Information Retrieval. The development of models,
tools and methods has significantly benefited from the availability of reusable
test collections formed through a standardized and thoroughly tested
methodology, known as the Cranfield paradigm. Constructing these collections
requires obtaining relevance judgments for a pool of documents, retrieved by
systems participating in an evaluation task; thus involves immense human labor.
To alleviate this effort different methods for constructing collections have
been proposed in the literature, falling under two broad categories: (a)
sampling, and (b) active selection of documents. The former devises a smart
sampling strategy by choosing only a subset of documents to be assessed and
inferring evaluation measure on the basis of the obtained sample; the sampling
distribution is being fixed at the beginning of the process. The latter
recognizes that systems contributing documents to be judged vary in quality,
and actively selects documents from good systems. The quality of systems is
measured every time a new document is being judged. In this paper we seek to
solve the problem of large-scale retrieval evaluation combining the two
approaches. We devise an active sampling method that avoids the bias of the
active selection methods towards good systems, and at the same time reduces the
variance of the current sampling approaches by placing a distribution over
systems, which varies as judgments become available. We validate the proposed
method using TREC data and demonstrate the advantages of this new method
compared to past approaches
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