=Paper=
{{Paper
|id=Vol-1945/paper_6
|storemode=property
|title=Twixonomy Visualization Interface: How to Wander Around User Preferences
|pdfUrl=https://ceur-ws.org/Vol-1945/paper_6.pdf
|volume=Vol-1945
|authors=Giorgia Di Tommaso,Giovanni Stilo
|dblpUrl=https://dblp.org/rec/conf/eics/TommasoS17
}}
==Twixonomy Visualization Interface: How to Wander Around User Preferences==
https://ceur-ws.org/Vol-1945/paper_6.pdf
Twixonomy Visualization Interface:
How to Wander Around User Preferences
Giorgia Di Tommaso, Giovanni Stilo
Sapienza University
Rome, Italy
ditommaso@di.uniroma1.it, stilo@di.uniroma1.it
Abstract. User interfaces have become essential tools for a user to in-
teract with a recommender system. In the two most common settings,
the user interface either helps users to collect their preferences, or to pro-
vide an explanation of the generated recommendations. In this paper, we
present the Twixonomy Visualization Interface, a tool that allows users
both to explore their preferences and to discover new ones. Preferences
are represented by a Wikipedia Category DAG connected with the ini-
tial (primitive) preferences implicitly or explicitly expressed by the user.
Our tool can be considered as an integration of a recommender system
since, by exploring the DAG, the user can both analyse the connections
between his/her preferred items and other semantically related items or
categories, and understand the motivations for new, serendipitous rec-
ommendations.
Keywords: Recommender system; user interface; twitter; social media;
data mining; graph; pruning; semantic; user profiling.
1 Introduction
The scope of Recommender System (RS) is to provide suggestions, or directing
a person to a service, product or content of interest [3]. It is widely-known that
the effectiveness of a recommender systems cannot only be measured by its
accuracy, but there are other elements that play a role, in order for the user to
trust the system and perceive it as an advice-giver [22]. In his ACM “Intelligent
User Interfaces” tutorial [10], Konstan highlighted the relevance that the user
interface has, in order to make a recommender system understandable by the
users. Indeed, when a user can visualize and contextualize the recommendations
with her preferences, it is more likely that she will accept what is recommended
to her.
In [4, 5], we presented a method for large-scale analysis of Twitter users sup-
ported by a hierarchical representation of their interests, which we call a Twixon-
omy. In order to build a population, community, or single-user Twixonomy we
first associate ”topical” friends in users’ friendship lists (i.e. friends representing
an interest rather than a social relation between peers) with Wikipedia cate-
gories. A word-sense disambiguation algorithm is used to select the appropriate
Twixonomy Visualization Interface 33
wikipage for each topical friend. Starting from the set of wikipages represent-
ing the main topics of interests of the initial Twitter population, we extract all
paths connecting these pages with topmost Wikipedia category nodes, and we
then prune the resulting graph efficiently so as to induce a direct acyclic DAG
graph and significantly reduce over ambiguity. This graph is the Twixonomy.
In this paper, we are going to present a tool, called Twixonomy Visualiza-
tion Interface (TVI), which allows a user to explore his/her Twixonomy, under-
standing what his/her interests are, what are the high-level Wikipedia categories
associated to these preferences, and the relationship between these preferences.
Moreover, it allows to compare the Twixonomy of a user with that of another
Twitter profile, thus having also a list of the shared interests in terms of cate-
gories, and a compact similarity measure (known as affinity score) between the
two Twixonomies.
By allowing the users to visually analyze their preferences and discover new
ones in terms of categories they are associated to will be useful from multiple
perspectives. Indeed, by seeing the correlation between the categories repre-
sented in the Twixonomy and the preferences they explicitly expressed, it is
more likely that the user will trust the system and accept the categories as rec-
ommendations of something they might be interested in. As a consequence, these
categories can be directly employed as an information source by a recommenda-
tion algorithm, thus broadening the preferences of the users and generating more
rich recommendations, certainly involving new items and possibly also surprising
(serendipitous) ones.
2 Related Work
This section presents the works related to our purpose, mostly focusing on rec-
ommender system in social networks and on the role of the user interface when
producing recommendations to the users.
Recommender system in social networks In the Social Networks literature there
is a considerable number of works in which users’ interests are extracted for
some specific purpose, like detecting trending topics, i.e., topics that emerge and
become popular in a specific time slot. Trending topics are extracted to produce
a recommendation [11, 6, 13], to model users’ expertise [21], or to detect shared
interests (e.g., events) that are predominant in a given time span [12]. The large
majority of scholars are concerned with user recommendation, a task for which
many solutions have been presented in literature (see [11] for a survey); since
they model user interests regardless of specific applications.
The studies more closely related to our work are [8] and [2]. In the former,
the authors aim to derive a categorial representation of users’ interests using
Wikipedia. First, named entities are extracted from the text in micro-blogs,
then, Wikipedia pages, named primitive interests, are associated to each named
entity. To select a reduced number categories, denoted as hierarchical interests,
spreading of activation [1] is used on a pruned version of the Wikipedia graph,
34 Giorgia Di Tommaso, Giovanni Stilo
where active nodes are initially the set of primitive interests. Note that, despite
their name, hierarchical interests are not hierarchically ordered: rather, they are
a set of mid-high level categories. The second related work is [2], where, similarly
to us, users’ interests are inferred at a large scale. This system, named Who Likes
What, was the first system capable of inferring Twitter users’ interests at the
scale of millions of users. First, the topical expertise of popular Twitter users
is learned from the names and descriptions of Twitter lists in which such users
actively participate. Then, the interests of users who subscribe to at least 3
expert users are transitively inferred. Who Likes What is also deployed online
with a web interface; we are going to analyze further which are the differences
and advances between the two systems.
User interface and recommender systems The user interface plays a crucial role
in the way the users perceive the recommendations [16]. Indeed, it is either
employed in a recommender system to collect preferences from the users [15],
or to provide a better understanding of the recommendations and allow a user
to explore them [18, 20] (our proposal fits with the second scenario). In [19], a
user-centric framework, called ResQue measures the user experience with a rec-
ommender system. Knijnenburg et al. [9] studied five interaction methods of the
users with a recommender systems and showed that users have different preferred
methods of interaction. SmallWorlds [7] provides a graph-based explanation of
the recommendations generated with a collaborative filtering approach. SetFu-
sion [17] gives the users the possibility to control a hybrid recommender system,
by setting the relevance that each component should have when generating the
recommendations.
Contributions Our approach provides a user interface to allow the user to get
in touch and explore his/her preferences. An interface that provides this useful
informations was never been proposed. With respect to the analyzed bibliogra-
phy, our main contribution is the visualization and the comparison between one
or more Twixonomies, providing the following advantages:
– it allows to visualize the interests of single users, communities and popula-
tions in a easily browsable and interpretable way, especially when compared
with the large number of unstructured and fine-grained topics extracted from
textual features in messages and lists descriptions (like in [2]);
– it allows to compare several Twixonomies giving a precise measure of seman-
tic symilarity;
– it is also designed as a predictive system that aims to discover the interests
not declared directly from the users and to recommend new ones on these
basis, also in the presentation aspect.
3 Proposed Approach
This Section first briefly describes the Twixonomy algorithm as described in
[4], and then discuss the functionalities exposed by Twixonomy Visualization
Interface (TVI).
Twixonomy Visualization Interface 35
3.1 Twixonomy Algorithm
Figure 1 provides a general picture of what the Twixonomy Algorithm produces.
For every user p (@JohnDoe) of a population P with a Twitter account, we
consider his followees (@OrlandoMagic, @Newsweek, @JohnMom, etc.). Some
of these followees are genuine friendship or parental relations (@JohnSister,
@JohnMom), some are possibly non-reciprocated relations directed towards ”au-
thoritative” Twitter accounts, indicating an interest of our user, rather than a
peer relation. In this case, it is likely that such followees have a correspondent
Wikipedia page, as shown in Figure 1. Starting from these pages, we use the
Wikipedia Category Graph to induce a full-fledge taxonomy, the @JohnDoe’s in-
terests Twixonomy. In Figure 1, dotted edges represent longer hypernymy paths
in the Twixonomy, that have been cut for the purpose of space.
A Twixonomy can be created both for single users, communities, and for the
entire Twitter population. Since a category in a Twixonomy can be associated
to all the users sharing that interest, the Twixonomy enables semantic group
profiling and recommendation.
Given a population of Twitter users P (possibly, |P | = 1), we generate the
Twixonomy in three steps:
1. First, we extract from the profiles of each user p in P the set F of followees,
such that each u in F is followed by at least one user p in P . Note that the
sets P and F are different, though possibly overlapping. We then create a
mapping between users u ∈ F and wikipages in Wikipedia, if any;
2. Next, we extract all paths connecting these pages with topmost Wikipedia
category nodes in the Wikipedia Category Graph, thus inducing a graph
W G;
3. Finally, we apply a graph pruning algorithm to remove cycles and reduce
multiple inheritance, a problem that often cancels the advantage of adding
semantics [14].
3.2 Functionalities
The Twixonomy Visualization Interface provides the following four main func-
tionalities:
1. Search Bar allows to the user of the visualization interface, to search for
a specific Twitter’s profile. The user inserts into the search bar a query
string and the system shows a list of Twitter’s profiles that best match the
requirements. The user can select one of the proposed profiles to visualize the
related Twixonomy. The search bar is presented to the user in the landing
page and it is also available in all the other views of the interface.
2. Graph Visualization represents the core of the Twixonomy visualization
Interface. The Twixonomy is visualized in this part of the view (bottom-
right of Figure 2) as a graph and it is always possible to select between these
three sub-functionalities:
36 Giorgia Di Tommaso, Giovanni Stilo
Twixonomy
Society Sports Culture
Economics Basketball Mass
media
Basketball
teams
National Basketball
Association teams
Orlando American
Magic magazines
USA American news
Economics Orlando Magic ABC News
basketball magazines
organizations players
coaches
wiki:en: wiki:en:
wiki:en: wiki:en: wiki:en: wiki:en:
World Good wiki:en:
John Orlando Dwight Time
Econ. Morning Newsweek
Calipari Magic Howard (magazine)
Forum America
@davos @UKCoachC @Orlando_ @Dwight @GMA @TIME
@Newsweek
alipari Magic Howard
Anonymize
d user
@JohnSister @JohnMom
@JohnDoe
Fig. 1. Example of Twixonomy for a single user
(a) Network displays the Twixonomy graph of the selected user exactly as
showed in Figure 2. In this type of visualization, nodes are disposed
using a circular layouts. Each type of node has its own logo: nodes with
Twitter logo indicate the topical friends; the associated Wikipage nodes
are identify by the Wikipedia W logo, and the other nodes in the network
are those inferred from Twixonomy. In our example it is possible to note
that the principal interest of the selected Twitter’s profile is related to
the photography domain.
(b) the Hierarchical view allows a user to navigate the Twixonomy of the
selected profile using a hierarchical layout. Nodes are presented without
any icons and only the text label is associated to each node. Nodes are
Twixonomy Visualization Interface 37
Fig. 2. Snapshot of Twixonomy Visualization Interface
organized in levels; this view is designed to make it easier to understand
the relations between nodes.
(c) the Compared view allows a user to navigate the Twixonomy of two se-
lected profiles at the same time. Nodes of each Twixonomy are disposed
using circular layouts and the same icons of the Network view are used.
This view it is designed to better understand what are the semantic dif-
ferences and the common points between two profiles.
For all the above exposed views (Network, Hierarchical, Compared ), it is
possible to zoom in and zoom out the graph, move nodes and edges and pan
the view.
In almost every case the Twixonomies are too big to allow a readable navi-
gation of the graph. To deal with this issue we allow the user of the platform
to select the depth level of the visualized Twixonomy.
For each interest node is also possible to read the related Wikipedia de-
scription and it is also possible to share the visualized Twixonomy over the
principals social media platforms (Twitter, Facebook).
3. Interest List is presented on left part of the interface and contains all the
nodes of the Twixonomy when the selected view is Network or Hierarchical
. When the selected view is Compared only the nodes shared between the
compared Twixonomies are displayed. In both cases the Interest List can be
downloaded as a textual file.
4. Affinity Score is presented to the user on the top left part of the interface
(see Figure 2), when two Twixonomies are selected (Compared functionality).
The Affinity Score is the compact representation of the semantic similarity
between the selected profiles. The semantic similarity is computed using the
38 Giorgia Di Tommaso, Giovanni Stilo
following formula as proposed in [4]:
PNk
i=1 w(dAk
i
) × w(dBik )
SemSym(A, B) = qP qP (1)
Nk Nk
i=1 w(dAk
i
)2 × i=1 w(dBik )
2
In the formula, A and B are the semantic vectors, extracted from each
Twixonomy, associated to users a and b , Aki , i = 1 . . . nk , is the i-th boolean
argument of A and is non-zero if the Twixonomy of a includes the node cki
of the population’s Twixonomy. The index k = 0 . . . K is the generalization
level (k = 0 indicates Wikipages, as shown in Figure 1), that we also denote
as Lk , and nk = |Vk | is the total number of nodes in the Twixonomy up
to Lk . Furthermore, dAki = k is the length of the minimum path connecting
cki with a leaf node1 . Finally w(d) = β · e−α·(d+1) is a weight function with
exponential decay2 , where we empirically set β = 2 and α = 0.5. In formula
(1), non-zero terms in the numerator are those for which Aki = Bik , how-
ever the contribution of a match exponentially decays with the distance k
of matching categories from leaf nodes. The higher is the value of SemSym,
the more are the shared interests between the selected users.
4 Recommendation Purpose
As we previously remarked, user interface plays a crucial role in the way the users
perceive the recommendations. Indeed, it is either employed in a recommender
system to collect preferences from the users, or to provide a better understanding
of the recommendations and allow a user to explore them. The Twixonomy
Visualization Interface is designed not only for descriptive analysis of the interest
inferred from Twitter users profile but and even as a predictive system that aim
to discover the interests not declared directly from the users and to recommend
new ones on these basis.
In a typical use-case, a user starts the investigation process by visualising
her own Twixonomy using the Network view (point 2a of the previous section).
With this type of visualization, a user should discover intuitively (also guided
by different icons) which are the categories that dominate his interests. Using
this visualisation mode is then possible, searching in the Interest List, to find
a specific interest even if the Twixonomy is huge. By taking a closer look to a
specific node, it is then possible to understand how strong is the relation with
neighbor nodes. If the user would like to know which is the generality level of
one node, he can switch to the Hierarchical view (point 2b). Indeed, by using the
Hierarchical view, it is possible to understand what is the type of relationship
of the Twixonomy and see which categories are higher than others.
1
note that leaf nodes matches have d = 0
2
with w(d) we mean generically either w(dAk ) or w(dB k )
i i
Twixonomy Visualization Interface 39
To inspect a second use-case, suppose that a user wants to understand how
close he is (in term of interests) to another Twitter’s profile. Then is possible
to select the Compared view (Secton 2c) to immediately take a look at two
Twixonomies at the same time. The user then can visually inspect what are the
different or the shared interests between the compared users. If it is not possible
to effectively understand which are the similar interests of the Twixonomies
(due to the large amount of interest of both profiles), then the Interest List
helps the user to have a more compact representation of the similarity exposed
by the profiles, by highlighting only the shared interests. Simultaneously, the
Affinity Score offers to the user the fastest way to understand what is the level
of similarity exposed by the selected profiles.
At the end of the process the user can understand not only his preferences
but also how these are generated, what is the level of generalisation between
them, and which of them depends by others. User that also have to compare
their interests to those of another Twixonomy, can also understand which is
their affinity to another profile and which are the potential new preferences that
they should be interested in.
5 Conclusion and future work
In this paper, we presented the Twixonomy Visualization Interface, a tool to
explore and analyze a hierarchical representation of the user preferences, called
Twixonomy. Compared to [2], that presents users’ preferences as simple tag-
cloud, our interface presents to the user a more detailed informations; in addition
Twixonomy Visualization Interface enables to wonder around the interest and
compare own interests with those of the community. We are actually thinking
of many possible improvements of the platform. First, we plan to embed the
Twixonomy Visualization Interface in the Twitter platform in a way that should
be easily possible to build the Twixonomy of a community (list, in Twitter termi-
nology). Another improvement that we plan to develop is to give the possibility
to each user to evaluate their Twixonomy by expressing a degree of precision
and allowing feedbacks on the quality of the interface. Another on-going study
regards the user interface evaluation. In particular, we would ask to users which
views is more useful between the ones that are already available and the ones
that are under developments.
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