=Paper=
{{Paper
|id=Vol-1705/07-paper
|storemode=property
|title=Recommendation Centre: Inspecting and Controlling Recommendations with Radial Layouts
|pdfUrl=https://ceur-ws.org/Vol-1705/07-paper.pdf
|volume=Vol-1705
|authors=Lucio Davide Spano,Gianni Fenu
|dblpUrl=https://dblp.org/rec/conf/eics/SpanoF16
}}
==Recommendation Centre: Inspecting and Controlling Recommendations with Radial Layouts==
https://ceur-ws.org/Vol-1705/07-paper.pdf
Recommendation Centre: inspecting
and controlling recommendations with
radial layouts
Gianni Fenu Abstract
Department of Mathematics and In this paper we propose to use radial layouts for
Computer Science representing the matching between the user’s interest and
University of Cagliari
particular objects and/or categories. The technique
Via Ospedale 72, 09124,
supports the visualization of different data: we discuss here
Cagliari, Italy
fenu@unica.it
the relationships on social networks, the related videos on
YouTube and topics in Wikipedia. The user can change the
position of the object in the representation, which can be
used in recommender systems for providing a fine-grained
Lucio Davide Spano control over its internal preference representation.
Department of Mathematics and
Computer Science ACM Classification Keywords
University of Cagliari H.5.m. [Information Interfaces and Presentation (e.g. HCI)]:
Via Ospedale 72, 09124,
Miscellaneous
Cagliari, Italy
davide.spano@unica.it
Author Keywords
Human Computer Interaction, Recommendation Systems,
Visual Interfaces, Radial Layout, Inspection, Control
Introduction
End users often see recommender systems as black boxes,
which suggest them objects, people or concepts while they
are trying to find something inside a huge amount of data.
Copyright is held by the author/owner(s).
EICS’16, June 21-24, 2016, Bruxelles, Belgium. On the contrary, recommender systems have difficulties in
collecting the user’s opinion on the suggested contents,
since they mostly rely on explicitly expressed preferences,
which are known to be biased [1]. Explicit preferences
54
express love or hate, without helping much for intermediate circles. The main node is positioned in the layout centre: it
values. In addition, how to collect the information (e.g. represents the person, object or concept the user is
through rating scales) has an impact on the overall currently focusing-on. The different concentric circles give
recommendation performances [3]. immediately a feeling of the distance between the main
node and the other ones.
Our position with respect to this problem is that advanced
techniques coming from the Human Computer Interaction Currently, the visualization displays only the nodes that are
field may help both the system and the users. A possible directly connected with each other. This means that,
solution is to make the two communication endpoints more differently from the original version in [15], the circles are
transparent to each other. If the user would be able to not related to the graph depth, but it represent a weight
understand, through a simplified representation, how the associated to the edge.
recommender system is currently “reasoning” while
providing suggestions, she would be encouraged in fixing More in detail, the position of a node inside the visualization
possible prediction errors. On the other hand, the fixing depends on two factors. The first one is related to the
action can be exploited by the system not only for changing “distance” we want to represent, e.g. how many times we
a parameter related to a single user, but also for updating interact with a social network friend, how close a topic is
future predictions, either for the same or for similar users. related to another in Wikipedia, etc. We can define different
ways for calculating such distances and consequently
We developed a visualization technique for displaying a assign a value graph edges, according to the considered
summary of the social network interactions through a radial domain. Such definition would position continuously the
layout [5]. The user can both inspect and control the different objects in the radial layout.
representation, and the content filtering is updated
accordingly. In this paper, we discuss how a similar In addition, we included a discretization step in order to help
approach may be applied to recommender systems, in the user in identifying the different levels of relationship,
order to support the end-users in understanding their while keeping the visualization tidy. Therefore, the object
internal state. In addition, the users should be able to position depends on discrete distance levels, whose
modify the position of the object in view. The system should number is established according to the application domain.
update its internal model accordingly. We describe two Both the distance and the levels are defined through two
early application prototypes and we define the direction of functions that control the visualization layout.
future work.
In the following sections we discuss the application of the
radial layout to different case studies.
Visualization
In this section we discuss the visualization technique, which
Example applications
exploits a radial layout [15] for showing the relationships
between object and/or users. It positions a set of nodes, Social Networks
each one representing an object, inside a set of concentric We show the first example in figure 1, where we
represented a user’s ego network on a social network
55
3. The friend likes one of the user’s post on her wall
4. The user likes one of the friend’s post on her wall
After this counting step, we normalized the distance value
by the maximum value of interactions with a single friend.
Such sum gives us a value between 0 and 1 that is higher
for friends that communicate with our user very often, and
lower for the others.
The visualization confirms the results in [10]: a user
communicates often with a small set of friends, while with
most of them has less than one interaction per year. In
figure 1 most people is contained into the last circles, while
in the inner ones are less crowded.
YouTube Videos
In this example, we allow the user to visualize the results of
keyword search on YouTube. The resulting visualization is
shown in figure 2. The icons are video key-frames, hovering
Figure 1: Social Network radial layout visualization the mouse on top of each video result, the tool shows more
information on the selected video, magnifying the key-frame
and showing the full title (the bigger icon in figure 2, top
part). Clicking on an icon, the tool plays the video, showing
according to an interaction distance between the main user it on a modal window.
(show in the centre) and his/her friends.
In this case, we defined the distance function according to
We represented each friend using a square icon including three different parameters, which we obtain invoking
the profile image. Each icon belong to a different circle services from the YouTube Data API v3 [11]:
according to the distance function value. The continuous
distance was defined counting the following events:
1. Relevance: match between the query and the result.
1. The friend comments one of the user’s posts on her 2. View count: number of times the video has been
wall watched by any user.
2. The user comments one of the friend’s posts on her 3. Date: publication date.
wall
56
The three parameters are considered hierarchically in order
to establish the visualization distance. This means that we
first consider the semantic matching, secondly a
crowd-based ranking of the different videos and then we
consider the content age.
Wikipedia
We considered to apply the visualization to the Wikipedia
content, in order to apply it in showing the semantic
distance between concepts. In this case we used the
Wikipedia API [13] for accessing the data.
Similarly to the previous example, we focused on the
visualization of a keyword search result. We considered the
following properties in order to define the distance function:
1. Query matching: the similarity between the Wikipedia
page and the keyword
2. Last page update.
With respect to the usual result list visualization of the
search results, the layout in figure 3 provides the user with
the possibility to understand how distant the results are from
each other. Indeed, the search result page shows the
ranking, but the matching-distance between the results is
not uniform. For instance, it is possible that the distance
between the 10th and the 11th is smaller than the distance
between the first and the second.
The graph nodes are represented through both the
Wikipedia article title and its thumbnail image. Since not all
articles have an associated image, we used the first image Figure 2: YouTube videos related to the “Queen” keyword
included in the article if any, otherwise we used a default
image, i.e. the Wikipedia logo.
57
As in the YouTube application, the tool shows a small
preview when the user overs the mouse on a node, showing
the full article title and a bigger thumbnail image (figure 3,
top part). In addition, if the user clicks on a node, the tool
shows the corresponding article (figure 3, bottom part).
Control features
The possibility to visualize a distance between friends or
objects according to the internal system representation is
useful for the user, since it helps her to understand what the
recommendation support has learned from the data
analysis. However, this is not enough: users may want to
change the system internal representation when she is not
satisfied with it.
This would have a twofold positive effect on the interaction.
On the one hand, the system would gain an explicit
feedback, and this would be useful for both creating a more
precise user’s model. In addition, the same feedback can be
propagated to similar users. On the other hand, the user will
be more satisfied with a system that allows her to change
the representation of her interests, which would result in
more relevant recommendations.
Considering this, we inserted in the visualization tool the
possibility for the user to change the node position. We
show an example of this manipulation in figure 4. The user
selects one of the nodes in the visualization, and then she
can change the position inside the distance levels either
dragging the node or changing the slidebar values.
Such action has an effect not only in the visualization, but it
can be exploited also by the recommender system for
Figure 3: Wikipedia keyword search
updating its internal representation. Indeed, the system may
invert the distance function and let the user to specify
directly the matching value, without the need for her to
understand how the system internally calculates it.
58
In figure 4 example, we show a sample case for such
manipulation. The user, inspecting his social network, sees
that one of his best friends is quite distant from him. They
do communicate few times through the social network, but
they see each other at least once or twice a day. So the
user decides to change his friend’s position. The system
updates his internal representation consequently.
This has an impact for instance on the content that the
social network application shows on the user’s news feed:
the content published by the considered friend should be
visualised immediately in the first positions, even if from the
collected data the interaction between the two users is
weak.
Conclusion and future work
In this paper we discussed a simple example application of
Human Computer Interaction techniques for increasing the
user’s understanding of a recommender system. In our
opinion, providing simple yet effective visualization of the
their internal state to the user may have different
advantages.
First of all, the user would be able to inspect the
recommender system state and to fix possible prediction
errors that cause incorrect suggestions. While the user
would receive better content, the recommender system
would learn from the user’s feedback and use it also for
similar users. In addition, the user would trust more a
system that explains how it suggests a content, with respect
to other ones where she cannot find out if it is relevant for
Figure 4: Distance control functionality
her or it is simply advertised.
We described an early application of a radial visualization
from the distances between users in the same social
network to contents such as videos and Wikipedia articles.
In addition, we discussed how control techniques on such
59
visualization may have impact on the recommender system [4] Gianni Fenu and Marco Nitti. 2011. Strategies to carry
data. and forward packets in VANET. In International
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Development techniques [12] that may be used for defining [5] Gianni Fenu and Lucio Davide Spano. 2013. Mobile
other internal aspects, such as recommendation algorithms Web Information Systems: 10th International
and data collection. In this case the user would not develop Conference, MobiWIS 2013, Paphos, Cyprus, August
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recommendation interface. http://dx.doi.org/10.1007/978-3-642-40276-0_11
[6] Giovanni Garibotto, Pierpaolo Murrieri, Alessandro
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