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Personalised Document Corpora Exploration
Abstract
In our research, we explore ways of enhancing
personalised retrieval of relevant documents in open
text document corpora. While a lot of research has been
done in document retrieval, most approaches look at
ways of improving single-step query answering.
Because the users might have more complex
information needs than they can express in a short
query, our scope is to research how a system can
actively assist the users in their exploratory search over
a sequence of steps (i.e., queries).
1. Introduction
Internet has become one of the main information
access points on the planet due to the great amounts of
text documents it provides. Two types of information
search have been identified: (i) look-up search (e.g., fact
retrieval) and (ii) exploratory search (e.g., learning,
investigation, analysis) [1]. Our focus is on enhancing
exploratory search with machine learning techniques.
Currently, there are very few approaches towards
information exploration, while most of research in the
area of personalised search focuses on the look-up
search [1]. However, since many users come to a search
engine for exploratory search, our research is directed
towards a system which is able to assist the user in his
“exploration” over an unlimited sequence of queries.
An important challenge is that new information items
become available every day and the system must be able
to accommodate any dynamic corpus of documents in
unsupervised fashion. Moreover, different users need
different information even when they ask the same
query, so user modeling is crucial for our work.
2. Our Approach
2.1 Domain modeling
In order to be able to guide the user towards
information items of interest, the system must keep a
“browsable” domain model, and constantly map the
user’s state to that model.
In our approach, the text documents are firstly
processed for extraction of probabilistic topic models.
Latent Dirichlet Allocation (LDA) is a well-known
algorithm for this purpose which we explore in this
setting. The obtained topics are then used to label and
cluster the documents. Many algorithms have been
researched for document clustering. Still, one challenge
is to identify for each user the level of granularity he can
internalise or is interested in. For example, the level of
details an expert feels comfortable with is not the same
with that of a beginner in the same subject.
Ioana Hulpuș, Conor Hayes
DERI, National University of Ireland, Galway
{name.surname@deri.org}
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Documents are also connected based on their
citation and bibliographic networks. The resulting
network structure enables the navigation of documents
based on authors and their citations. The bibliographic
links enable the identification of communities, which is
currently a highly researched topic. However, these
communities are created based on citations, and at the
same time, one of the most common ways of scientific
data exploration is by following citation paths. Thus, by
traversing only bibliographic paths, the users can get
stuck into the point of view of one community. Our aim
is to use the available research in the direction of
community detection and make the user aware of the
different communities dealing with his topics of interest.
2.2 Trace models and trace based reasoning
This model captures the “interaction traces” between
the user and the system. Trace-based reasoning (TBR)
has its root in case-based reasoning, as it solves new
problems by adapting past solutions (i.e., user traces).
Past traces can be used to infer the context of a user’s
search, which is very important for assessing relevance
of information. At the same time, machine learning can
be used on the repository of traces, in order to identify
patterns of information requests. The idea behind this is
that if some documents or queries are constantly
accessed together by various users, they might be
related.
2.3 User modeling
User modeling is very much related to trace
modeling as each user has assigned his past traces. As
well, the system will try to capture the user’s level of
expertise and his topics of interest, through a mixed
initiative approach.
3. Conclusions
Exploratory search is still an under-studied area of
personalised information retrieval. In order to address
this topic, we plan to use machine learning for (i)
creating a browsable hierarchical structure of the
domain, (ii) inferring the user’s expertise, (iii) and to
identify latent document relations. This knowledge is
then used to guide the user and recommend documents
suitable to his information needs.
4. References
[1] Marchionini, G. (2006) Exploratory search: From
finding to understanding. Communications of the
ACM, 49(4), pp. 41–46.