![](bg3.png)
depending on the nature of the user event.
Specifically, the concept’s weight in the model will
either be increased for a positive event or decreased
for a negative event. The reinforcement weight is a
system parameter that controls the rate of change in
the weight during reinforcement.
6) If the feedback is positive, insert top N new
concepts from the feedback into the current model
with a default weight modulated by their relevancy
(e.g. term frequency).
7) The user model is divided into facets based on
pre-specified time interval or number of user events.
When the time interval expires or the specified
number of events has been processed, the current
facet is retired and a new facet will be created. The
facet size refers to the number of events in a facet.
It’s also a system parameter that may impact the
effectiveness of the system.
The UMS builds a user interest model for each
known chat user using the RAMA algorithm. It can
also build an information model for each chat room
using the same algorithm. Thus the room model can
be regarded as a team model for all participants of
the room. Due to space limitation, we will not go
into details about room model.
The chat user models form the basis for several
user-tailored information recommendation services.
We discuss these services next.
Based on user interest models, VIG service is to
identify and recommend to a chat user a VIG, other
system users with similar interest, information
needs, and expertise. VIGs can facilitate information
sharing and collaboration among warfighters
because they explicitly suggest to a user other like-
minded people they may talk to.
The VIG identification algorithm works by
comparing different facets of the user model (Alonso
& Li, 2010). The more facets are similar between
two users, the more similar they are. If none of the
facets are similar, the two users are not alike at all.
Cosine similarity, commonly used in the vector
space model, may be employed to compare the
similarity of two model facets (Salton et al. 1975).
The VIG size is a system parameter and refers to the
number of member users to include in a VIG during
computation.
The proactive Knowledge Recommender (aka
KnR) is another service enabled by the user models.
The system can automatically generate search
queries using the model and retrieve relevant
documents from the Web or databases on the user’s
behalf. Also powered by the user models is the Chat
Snippet Alerts service, which monitors concurrent
chat rooms and alerts the users with chat messages
that contain relevant events.
2.3 The Interfaces Module
The Web user interface (UI) is a simple Java
®
(Oracle America, Inc.) servlet-based client graphical
user interface (GUI) that displays the user models,
VIGs, and routed chat snippets, all of which may
have also optionally persisted locally in the
Checkpoint Repository. The Quick Access Panel
allows examination of user models and VIGs
through extensive visualizations.
The Chat GUI interface is used to manage live
XMPP or IRC chats (Figure 1). Chat servers and
room connections are configured here. Real-time
chats from multiple chat rooms are monitored.
Adaptive user interest models and dynamic VIGs are
generated for chat users. The user models and VIGs
can be visualized with different graph layouts.
The Web UI serves as a demonstration tool
showing much of the functionality of the system. It
works like a simple web page but provides access to
the list of user models that are being stored in the
database. It also allows one to look at a specific user
model, construct a VIG, or inspect chat snippets that
have been routed to users, and learn more about the
system itself.
The Checkpoint Repository periodically saves
the session information for each user for offline
assessment including the user model and the VIG.
The Quick Access Panel is a graphical interface
for examining the products (i.e., user models and
VIGs) generated and stored by the system core. It
provides a variety of visualizations for these
products.
3 EVALUATION STUDIES
To assess the performance of the VIG functionality,
we designed a user-centred study and ran an
experiment a using real operational chat data set. In
this section, we describe them in detail.
3.1 User-centered Evaluation
For this study, we had an intern (political science
major) to generate a chat stream. She played a total
of six roles (Adams, Adrianne, Alycia, Charlotte,
Princess Adrianne, and Rachelle). The chats were
focused on three topics listed below.
1) T1 (BP oil spill): The impact of the BP oil spill
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