An Interactive System for Capturing Users’ Qualitative Preferences in
Recommender Systems
Kushal Dave and Malek Mouhoub
a
Department of Computer Science, University of Regina, Regina, SK, Canada
Keywords:
Recommender System, CP-net, Membership Query, Preference Eliciation, Dictionary.
Abstract:
We propose a new interactive system for eliciting and learning users’ qualitative preferences. These prefer-
ences are modelled as a conditional preference network (CP-net). The CP-net is a known graphical model
representing qualitative and conditional preferences in a compact form. User’s preferences are first captured
through a learning method based on membership queries. These preferences are then compiled into a list of
conditional preference statements. The CP-net is finaly generated from this list. We are also incorporating a
collaborative technique so that when a CP-net of a given user is generated, the latter will receive suggestions
based on similarities with other users.
1 INTRODUCTION
A recommender engine is a tool that collects a large
amount of data related to users’ satisfaction and de-
sires (Ikemoto and Kuwabara, 2019; Bobadilla et al.,
2013; Karimi et al., 2018). These data are then used
to provide suggestions and recommendations to other
users. For instance, in social media such as Face-
book and Linkedin users are given recommendations
on friends and connections respectively. On the other
hand, streaming services such as Netflix recommend
movies meeting users interest, online shopping sys-
tems like Amazon and eBay suggest products of sim-
ilar interest, and tinder provides personalized rec-
ommendation for partners. The recommender sys-
tem gives users suggestions of things with a similar
or relative interest that they might like. All these
recommender systems work primarily with quantita-
tive preferences (expressed as numerical ratings) us-
ing Collaborative, Content-based or Hybrid Filter-
ing (Balabanovic and Shoham, 1997; Mohammed
et al., 2013; Adomavicius and Tuzhilin, 2005). There
are many scenarios however where qualitative prefer-
ences are preferred. The latter express ordinal pref-
erences which might be more natural than quantita-
tive preferences. Indeed, it is often more appropri-
ate to ask which option is more preferred rather than
providing specific scores for each option. This mo-
tivated us to explore recommendation systems in the
a
https://orcid.org/0000-0001-7381-1064
context of qualitative preferences. In this regard, we
propose a new interactive GUI for eliciting and learn-
ing users’ qualitative preferences through member-
ship queries. The learned preferences are then repre-
sented in a compact manner user the CP-net graphical
model.
The remaining of the paper is structured as fol-
lows. The next section reports on background ma-
terials related to recommender systems, preference
reasoning and preference learning. Section 3 de-
scribes our proposed approach and presents the pro-
posed GUI. Finally concluding remarks and ideas for
future works are listed in Section 5.
2 BACKGROUND
2.1 Preferences and Recommender
Systems
In this fast-growing world, you will get alternatives
and options for everything. As many alternatives and
options are there, it is often difficult to choose from.
Qualitative preferences over a set of available op-
tions order these options such that a more desired op-
tion comes before a less desirable option. The items
in this collection of options will be referred to as
the outcome.(Brafman and Domshlak, 2009). Pref-
erences are now part of the routine. Knowingly or
unknowingly, everyone has preferences in their day-
288
Dave, K. and Mouhoub, M.
An Interactive System for Capturing Users’ Qualitative Preferences in Recommender Systems.
DOI: 10.5220/0011292000003274
In Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2022), pages 288-295
ISBN: 978-989-758-578-4; ISSN: 2184-2841
Copyright
c
 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

to-day life. For example, one prefers tea over cof-
fee. Preferences direct our actions and everyday de-
cisions. In addition, preferences help to make prac-
tical reasoning. Preferences have been studied in
Logic, economics, operations research, Psychology,
philosophy, game theory, decision theory, and Artifi-
cial Intelligence (Doyle, 2004; Thomason et al., 2018;
Thomason, 2014). Preferences have an essential role
in knowledge representation (Boutilier et al., 2004),
multi-agent systems (Shoham and Leyton-Brown,
2009), recommender systems (Kostkova et al., 2014;
Adomavicius and Tuzhilin, 2005; Karpus et al., 2016;
Carvalho and Belo, 2016), and many more (Thoma-
son et al., 2018). In this context, a common goal is
to manage preferences when making automated de-
cisions as well as when making users’ recommen-
dations (Alanazi et al., 2019). The latter rely on
quantitative ratings elicited from users. Recommen-
dations are then suggested to other users according
to the following models: conditional filtering, collab-
orative filtering, content-based filtering, and hybrid
filtering techniques (Balabanovic and Shoham, 1997;
Mohammed et al., 2013; Adomavicius and Tuzhilin,
2005). More precisely recommendations are based on
collected scores and discovering similarities between
items in an item-based model, between users in a user-
neighbourhood model, or between users and items
in a user-item based model (Deshpande and Karypis,
2004; Adomavicius and Tuzhilin, 2005).
2.2 CP-net
In (Boutilier et al., 2004), Boutilier et.al. proposed
a qualitative graphical representation of conditional
qualitative preferences, under a ceteris paribus (all
else being equal) interpretation. The model is called
the Conditional Preference Network (CP-net) and is
capable of representing qualitative preferences in a
compact, intuitive and structural graphical manner.
Here, preferences are with or without conditions.
Preferences are defined with the succeeds ”≻” or pre-
cedes ”≺” symbol. If x is preferred over y with de-
pends on z then that will be denoted with the follow-
ing preference statement, z : y ≻ x or z : x ≺ y
Example 2.1. “I like coke when pizza is chosen, and
gingerale when burritos is selected” is a conditional
preference as the selection of coke and gingerale,
depends on the chosen main dish. This example will
be denoted with the following preference statements.
Burritos: gingerale ≻ coke
Pizza: coke ≻ gingerale
Definition 1. A CP-net over a set of variables V =
X
1
, ..., X
n
is a directed graph G whose nodes (vari-
ables) are annotated with conditional preference ta-
bles CPT (X
i
) for each X
i
∈ V . Each conditional
preference table CPT (X
i
) associates a total order ≻
i
u
with each instantiation u of X
i
’s parents Pa(X
i
) = U
(Boutilier et al., 2004).
CP-net is a compact representation of conditional
preference statements. The CP-net can be expanded
to an induced graph, where nodes represent all possi-
ble outcomes while arcs denote the dominance rela-
tion between them.
Example 2.2. Consider the CP-net in Figure 1 that
expresses preferences over meal configurations. This
network consists of two variables F and D, standing
for the food and drink, respectively. Here, burritos
(F
b
) is unconditionally preferred to Pizza (F
p
), while
the preference between coke (D
c
) and gingerale (D
g
)
is conditional on the value assigned to F.
Figure 1: A CP-net (left) and its corresponding induced
preference graph (right).
Example 2.3. Consider the CP-net in Figure 2 that
expresses preference for dressing configurations. This
network consists of three variables T, B, and S, stand-
ing for the top, bottom, and shoes, respectively. Light
blue top(T
b
) is strictly preferred to white top (T
w
),
while the preference between Khaki (B
k
) and the Navy
(B
n
) bottom is conditional on the selection for tops.
Navy bottoms is preferred with light blue tops while
khaki bottoms is preferred with white tops. The pref-
erence for black (S
l
) and burgundy(S
u
) shoes is con-
ditional on the selection of bottom; black shoes are
preferred with navy bottom and burgundy shoes are
preferred with khaki bottom. Below is the representa-
tion of CP-net and induced graph.
The graphs representing the above CP-nets are
acyclic. In cyclic CP-nets, preferences are preferred
over one another in a cycle. Cyclic CP-nets can be in-
consistent (leading to a cycle in the corresponding in-
duced graph). Figure 3 (Domshlak, 2002) shows two
CP-nets represented with the same graph depicted in
Figure 3 (e). The first one with the CP-table in Fig-
ure 3 (a) is consistent as its induced graph shown in
Figure 3 (b) is acyclic. However, the CP-net with the
An Interactive System for Capturing Users’ Qualitative Preferences in Recommender Systems
289

Figure 2: A CP-net representing the preferences for Top,
Bottom & Shoes (left) and its induced preference graph
(right).
CP-table in Figure 3 (c) is inconsistent as its induced
graph in Figure 3 (d) is cyclic.
While cyclic CP-nets come with a challenge of
checking for their consistency, they might be useful
to express preferences in a natural way, in some ap-
plications (Boutilier et al., 2004; Domshlak, 2002).
Figure 3: Consistent and inconsistent cyclic CP-nets.
Another type of CP-net are separable CP-nets.
A separable CP-net is one in which the dependency
graph has no edges. Essentially, this means that pref-
erences over the domain of every attribute are inde-
pendent of preferences over the domain of any other
attribute.
2.3 CP-net Learning
There are mainly two ways to learn CP-nets
(F
¨
urnkranz and H
¨
ullermeier, 2011): passive learning
and active learning. Passive learning corresponds to
learing from historical data. Basically, the learner
tries to concentrate on a small amount of data that
was previously supplied by the user to construct the
CP-net. Active learning, on the other hand, is typi-
cally employed in real-time with the user when ask-
ing questions about their preferences and constructing
a CP-net based on their responses (Chevaleyre et al.,
2011).
In (Chevaleyre et al., 2011), the authors addressed
a wide range of challenges pertaining to CP-net learn-
ing. At first, the idea is to develop a CP-net such
that the user’s preference relation corresponds to its
induced preference relation, or is a closer approxima-
tion to the user’s complete preference relation.
In (Koriche and Zanuttini, 2010), the authors in-
troduced two algorithms to learn CP-nets with equiv-
alence and membership queries. It was stated that CP-
nets are not learnable with equivalence query alone,
whereas acyclic CP-nets are attribute-learnable when
both equivalence and membership queries are avail-
able. Furthermore, the authors introduced two sepa-
rate algorithms to learn acyclic CP-nets and tree CP-
nets using equivalence and membership queries. Ba-
sically, the main idea is to start with an equivalence
query and then search for parents accordingly using
membership queries. This was the first attempt using
active learning for graphical preference languages.
In (Guerin et al., 2013), the authors introduced
the process of learning CP-net in two separate phases.
The first phase establishes a separable CP-net base by
asking the user to specify a default choice for each ac-
cessible variable and therefore creates the initial im-
pression of the CP-net. In the second step, the CP-net
is refined by eliciting user preferences for pairs of out-
comes and establishing conditional relationships.
In (Alanazi et al., 2019), the authors report on
the first study that exactly calculates the sample com-
plexity for learning qualitative preferences, through
acyclic CP-nets. The bounds of the VC dimension,
the teaching dimension and the recursive teaching di-
mension have been defined.
In (Allen, 2015; Allen et al., 2017) the authors
describe a local search algorithm for tree-shaped CP-
nets. They also proved that the algorithm also works
in the presence of noise. The proposed algorithm fol-
lows passive learning and uses pairwise comparison
of data to generate a target CP-net. The authors intro-
duced encoding for acyclic CP-nets with constraints
on indegree and multivalued domains, and a separate
encoding that is specific to tree structured CP-nets
with binary domains and complete tables. While pre-
vious research from (Alanazi et al., 2019) also intro-
duced a learning algorithm for tree structure CP-net,
the method in (Allen, 2015; Allen et al., 2017) is ca-
pable of handling noisy comparison sets robustly.
In (Mohammed et al., 2013), the authors proposed
a new interactive system that enables users to express
their needs and desires. Managing constraints and
preferences is one of the major components of the
proposed system. The user is allowed to select prefer-
ences as qualitative or quantitative or both. Data min-
SIMULTECH 2022 - 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
290

ing association rules technique is used for preference
suggestions based on learning from other clients.
In (Labernia et al., 2017), the authors suggest an
online learning technique of acyclic CP-nets where
observations arrive as a stream and cannot be mem-
oized as in a recommender system whereas user’s
clicks could generate them. Noise can be caused by
many factors: distracted user, corrupted data, unob-
served variables, etc. The author introduced an al-
gorithm to observe CP-net comparisons online. The
author analyzed the proposed algorithm by showing
its usefulness using synthetic and real-world datasets.
In (Labernia et al., 2018), the authors introduced
a query-based learning CP-net approach, where an in-
spiration from (Labernia et al., 2017) was used to de-
compose the procedure into general learning phase
and parent search phase, based on equivalence and
membership queries.
In (Liu et al., 2018), the authors introduced de-
pendent degrees to calculate the dependency relation-
ship among attributes. In this method, the authors use
passive learning for generating CP-nets. Additionally,
three algorithms were proposed. The first algorithm
filters the preference database, while the second al-
gorithm finds the degree of dependence of pairwise
attributes which uses a filtered preference database
from the previous algorithm. The third algorithm task
is to generate the CP-net. This algorithm uses the pre-
vious tasks to calculate the degree of attribute depen-
dence for each pair. Then this method is applied on a
database with 18 attributes. The results for generating
CP-nets for those data where reported.
In (Ali, 2019), the author propose an algorithm to
aggregate a set of CP-nets into a single summary CP-
net. Generated CP-nets most of the time are cyclic in
nature as each user has their own preferences. Along
with generating CP-nets, the algorithm also calcu-
lates relative swap disagreements (disagreements over
preference selection by each user on the basis of total
swap disagreements). Generated CP-net is more fo-
cused to get an idea of overlapping or disagreements
of user preferences. And also these algorithms are ap-
plicable on a set of CP-nets only.
2.4 Membership Query with Swap
Examples
As explained in (Chevaleyre et al., 2011), in active
preferences learning, the user has preferences in mind
among the options but does not know how to repre-
sent that structure in CP-net. In this case, the user
helps the learner to answer preferences through Mem-
bership Queries (MQs). More precisely, the learner
asks the user MQs such as: “do you prefer ‘x’ over
‘y’?” Where ‘x’ and ‘y’ are outcomes chosen by the
learner. The width of MQ (x,y) needs to be set. This
width is the number of entries (attribute values) on
which the outcomes x and y differ. A MQ of width 1
is called a swap membership query. The primary is-
sue with MQs is that they are rarely comparable with
a high width value; otherwise, the learner is forced
to ask polynomial queries. The following is an ex-
ample explaining membership queries with swap ex-
amples. Let us recall the dressing example where the
user is picking Top, Bottom & Shoes from different
colors. Assume we choose light blue top(T
b
), navy
bottom(B
n
), black shoes (S
l
) over white top(T
w
),
navy bottom(B
n
), black shoes (S
l
). Here, among all
selections only Top light blue differs from white and
all other characteristics are the same. Similarly, if
we choose light blue top(T
b
), navy bottom(B
n
), black
shoes (S
l
) over light blue top(T
b
), navy bottom(B
n
),
burgundy(S
u
) then in that case black shoes (S
l
) is dif-
ferent from burgundy (S
u
) and the remaining charac-
teristics are the same. During the MQs learning pro-
cess, the Users will be asked all possible combina-
tions to get precise results of the target CP net. In
most cases, limiting the characteristics helps to re-
duce membership queries and also helps users by giv-
ing them limited preferences instead of polynomial
queries.
2.5 Dictionary
A dictionary is a data structure used to store a
collection of items. This data structure allows the
use of a wide variety of data types including strings
and tuples as the index. These indices are known as
keys, and are used to refer to a particular value in a
collection. This means that the ”key” and ”value” of
each entry in a dictionary are paired up and stored as
a series of key-value pairs. This is a container for a
collection of objects structured in key-value pairs that
can be utilized for a variety of purposes
1
.
Many operations are often supported by dic-
tionaries, such as retrieve a value (based on the
programming language, attempting to retrieve a
missing key may provide a default value or throw
an exception), inserting or updating a value (typi-
cally, if the key does not exist in the dictionary, the
key-value pair is inserted; if the key already exists,
its corresponding value is overwritten with the new
one), remove or delete a key-value pair, and test or
verify for existence of a key. Most programming
languages with dictionaries support iteration over the
keys or values in a dictionary. Dictionary entries are
written in curly brackets (). A colon (:) separates the
1
https://infx511.github.io/dictionaries.html
An Interactive System for Capturing Users’ Qualitative Preferences in Recommender Systems
291

Figure 4: System Architecture.
key and value in each key-value pair, and a comma
(,) separates each element (pair) in the dictionary.
Below is an example of a Dictionary.
{ ‘ f
1
’ : ‘apple’,
‘ f
2
’ : ’orange’,
‘ f
3
’ : ‘banana’}
Here f
1
, f
2
, f
3
are keys to apple, orange and
banana respectively.
Dictionary in python supports different operations
including easy access to key and values, adding or
changing dictionary element, removing element from
dictionary, combining two dictionaries, and also pro-
vides in-built functions of length, sort, compare, copy,
etc.
The following example highlights one of the com-
prehension features of python using dictionaries and
range function.
Qubes = { x: x*x*x for x in range (5)}
print(Qubes)
#Output
{0: 0, 1: 1, 2: 8, 3: 27, 4: 64}
This above code can be written as
Qubes = {}
for x in range(5):
Qubes[x] = x*x*x
print(Qubes)
#Output
{0: 0, 1: 1, 2: 8, 3: 27, 4: 64}
A Dictionary can keep several values in an integrative
manner, which is the reason we adopted it to store in-
put, output, and in between values in our proposed
system. In the following sections, we will show how
these dictionaries store data and pass them to func-
tions for manipulation and creating desired output.
3 PROPOSED SYSTEM
The architecture of our proposed system is depicted
in Figure 4. The aim of the system is to generate a
CP-net by eliciting user’s preferences, and using dic-
tionaries and MQs with swap examples. The first step
is for the user to select the list of attributes and values
SIMULTECH 2022 - 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications
292

they are interested in. The user will then receive a set
of recommendations from similar users. The prefer-
ences of the user will then be elicited through a set of
MQs. The induced graph will then be incrementally
build while receiving user’s answers. Finally the CP-
net together with the induced graph will be presented
to the user, and added to the database for future rec-
ommendations.
Let us illustrate the overall process using a vari-
ant of Example 2.3. Assume we want to learn user’s
preferences for an outfit. When given the list of pos-
sible items, we assume the user has selected Top (T),
Bottom (B) and Shoes(S). The user will then be asked
to select the possible colors for each. This is done
through the related GUI of our system, depicted in
Figure 5. Here, the user can either select the colors
to consider (for each item) or choose not to select any
color. In the latter case, all selected colors will be
considered.
Figure 5: Color Selection.
Figure 6: Recommender System - User Selection.
Given the user’s selection for items and colors, the
system will display these choices as shown in Figure
6. The user will then be presented with a list of MQs
and will provide the answers accordingly, as depicted
in Figure 7.
Note that in the worst case scenario, the number
of queries to consider is equal to the total number of
swaps, n × d
n−1
, where n is the number of attributes
and d their domain size. In the case where we are
dealing with binary attributes values then the total
number is n × 2
n−1
. This number is justified as fol-
lows. If we have n variables then, if we decide to
flip one variable value, we will have 2
n−1
possibil-
ities (corresponding to all possible combinations for
the n − 1 variable values). When considering each of
the n variables, the total number will be n × 2
n−1
.
The system will then compile the MQs answers
and summarize them in an induced graph, as listed
in Figure 8. Here, each of the outcomes on the left
Figure 7: Recommender System - Preference Selection.
An Interactive System for Capturing Users’ Qualitative Preferences in Recommender Systems
293

of each row will dominate all the outcomes on the
right. For instance, the outcome on the left of the first
row, (Top = Blue, Bottom = Linen, Shoes = Black),
dominates the three outcomes on the right of the same
row.
Figure 8: Preferences - Induced Graph Nodes.
Using the induced graph depicted in Figure 8, we
will extract the (conditional) preference statements
for each of the three items. The result is listed in Fig-
ure 9.
Figure 9: Extracted preference statements.
Finally, from the preference statement in Figure 9,
we will build the CP-net depicted in Figure 10.
4 CONCLUSION AND FUTURE
WORK
We have proposed a new system for learning user’s
qualitative preferences through a friendly GUI. The
target model to learn is a CP-net and the learning pro-
cess is conducted through membership queries. The
system can be useful for preference elicitation, deci-
sion making systems, reasoning tasks, including e-
commerce, combinatorial optimization, multi-agent
Figure 10: Target CP-net to learn.
planning and agreement, etc. This is the first attempt
using features like dictionary, user interface, SVG,
membership query and membership queries to pro-
duce accurate CP-net on fly. The current version of
the system is focused on specific data but this can
be expanded, customized and moulded to accommo-
date different scenarios. Another future direction is
to look for new techniques to reduce the exponen-
tial number of MQs needed to learn the CP-net. One
way is to elicit constraints from the user, in addition
to preferences. This will help eliminating those MQs
that are inconsistent with the learned constraints. The
latter process can be effective when using the Con-
straint Satisfaction Problem (CSP) framework with
constraint propagation and variable ordering heuris-
tics (Dechter et al., 2003; Mouhoub et al., 2021). Fol-
lowing on the work in (Alkhiri and Mouhoub, 2022),
we also plan to define new similarity measures be-
tween CP-nets as this will help provide user’s sug-
gestions based on the their target CP-net. In this re-
gard, we will consider aggregating CP-nets methods
(Ali, 2019) that are needed every time we need to add
a new elicited CP-net. Aggregating CP-nets can be
represented using probablistic reasoning as done in
(Ahmed and Mouhoub, 2017). Finally we will rely on
a new CP-net variant (Ahmed and Mouhoub, 2020) to
consider both habitual behavior (represented as con-
ditional preferences) and genuine decisions.
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