A Recommendation System for Enhancing the Personalized Search

Itineraries in the Public Transportation Domain

Aroua Essayeh and Mourad Abed

Univ. Lille Nord de France, F-59000 Lille, France

UVHC, LAMIH, F-59313 Valenciennes, France

CNRS, UMR 8201, F-59313 Valenciennes, France

Keywords: Clustering, Learning Machine, Ontology, Public Transportation.

Abstract: In traditional transport information systems, the users must explicitly provide the information related to both

their profiles and travels to receive a personalized response. However, this requires, among others, an extra

effort from user in term of search time. We aim to identify not only implicitly users’ information, but also to

anticipate their need even if some data are missing through a recommender system based on collaborative

filtering technique. In this work, the information related to users is represented using the ontology which

proved far more adequate model for representing semantically data.

1 INTRODUCTION

In the era of globalization, the development of the

intelligent systems has seen a rapid evolution due to

the emergence of new technologies. However, the

information systems suffer nowadays from the

proliferation of information. The information is very

heterogeneous and provided from various sources.

For this purpose, personalization plays a decisive role

in information systems. In public transportation field,

the main difficulty is to propose for a user the best

itinerary that fit with his preferences and profile with

reducing at the same time both search time and effort.

Moreover, most of these systems do not tackle with

the problem of missing information, in this case, the

user is forced to fill and to complete all needed

information. Furthermore, it will be more

sophisticated to prognosticate their requirements

instead of expressing them at every turn. Hence, this

will save time when browsing for the best result, and

will help novice user when finding their needs more

easily.

To this end, we propose a new strategy of

personalization based on reasoning on over two

ontologies related to user profile and transport

domain , we have to learn later from their past

interaction with the system in order to reformulate the

query and recommend a new personalized solution.

The paper has three main contributions. First, we

propose to model users’ profile by using the ontology

and we include some properties that we considered

important in the context of travel. Second, we use

jointly, inferences rules and fuzzy clustering

algorithm to anticipate user’s needs implicitly even if

some information seems important are missed

according to the stored histories. Third, this algorithm

is enhanced through a new dissimilarity measure to

handle the problem of heterogeneous data.

The rest of this paper is organized as follows: We

introduce first a background about the personalized

information in public transport field. Second, we

discuss the collaborative filtering approaches. Third,

we announce our motivation in the next section.

Then, we illustrate the different steps of our proposed

algorithm. Finally, we show the experimental results

and evaluations, we discuss some conclusions about

the benefits and limitations of our approach and we

outline some future works.

2 RELATED WORKS

2.1 Personalized Information System in

Public Transport

As defined in (Hagen 1999), personalization is “The

ability to provide content and services that are

tailored to individuals based on knowledge about

their preferences and behaviour”. In public

transportation field, the personalization has a

Essayeh, A. and Abed, M.

A Recommendation System for Enhancing the Personalized Search Itineraries in the Public Transportation Domain.

DOI: 10.5220/0006315904150423

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 1, pages 415-423

ISBN: 978-989-758-247-9

415

significant consequence when searching for

personalized itineraries. This itinerary must, among

others, respond to users’ preferences and needs. Many

approaches have dealt with the personalization in this

field. An MDA (Model Driven Architecture)

approach is applied in (Marçal de Oliveira et al. 2013)

in order to build a personalized itinerary for a user

giving him at the same time some services related to

his preferences. The weakness of this approach is in

the manual mapping used between the domain

ontology and the context model which let this system

not applicable in real cases and remain a theoretic

approach. In their study, (Moussa, Soui2 & Abed

2013) have introduced a multi-criteria decision

making approach to personalize a system in public

transportation. This method uses the ELECTRE

method; it focuses on achieving a compromise

between the different compensatory criteria.

However, this work considers some quantitative

information related to the travel and assumes

implicitly that all criteria are considered fully

comparable which is not always possible in complex

systems. In their recent work, (Bouhana et al. 2015)

have proposed a hybrid method based on CBR (Case

Based Reasoning) and ontology to personalize the

itinerary for stakeholders. Despite the efficiency of

the adopted methodology, some drawbacks are still

unresolved, among which the prediction of user’s

needs without involving their personal motivations.

2.2 Collaborative Filtering Approach

Besides the huge number of heterogeneous data, users

have some difficulties to express clearly their needs

in a significant timing. In practice, users need

recommendations because they do not have enough

knowledge to make an autonomous decision (Ricci ,

Rokach & Shapira 2015) or what is the response

relative to their request.

Collaborative filtering techniques aim to perform

personalized recommender system. Furthermore,

several works are found in the literature and have

investigated to identify recommender system that aim

to provide a personalized content for a target user. As

defined in (Liu et al. 2014), the recommender

system’s aims to “guess” the users’ preferences by

analysing their behaviours when interacting with the

system. In other words, it reveals historic users to

anticipate their needs. Nonetheless, such systems

suffer from both scalability, and the cold start

problem. This issue is addressed in the present paper.

Moreover, these systems require the determination of

the correlation between the user and the

recommended service. This latter is then another

challenge to mitigate. We identify the most known

techniques used to deal with this issue such as the

Pearson correlation technique (Resnick, Iacovou &

Suchak 1994), the constrained Pearson correlation

(CPC)-based similarity (Shardanand & Maes 1997),

the cosine-based similarity (Sarwar et al. 2000), and

the adjusted cosine based measures (Ahn 2008) to

identify the similar users that rated the same services.

Some approaches require aggregator model to

enhance decision system. For example, a combination

of the OWA (Ordered Weight Aggregator) and the

LSP aggregator (Logic Scoring of Preference) used in

(Moreno et al. 2013) to analyse the user’s interactions

with the system, and to identify the degree of interests

to promote decision making. Choquet integral is also

used in both (Bouhana et al. 2015) and (Bouhana et

al. 2013) for making a decision. AHP and OWA

aggregators are used in (Abolghasem Sadeghi &

Kyehyun 2009) to investigate the decision maker.

The major key of a personalized process is to

know what a user wants and also why he needs this

(Lakiotaki & Matsatsinis 2011). To deal with this

issue a single rating item for each item doesn’t offer

an appropriate understanding as it use a unique

criteria to predict his needs. This issue is addressed

by the use of a multi-criteria approach (Nilashi , bin

Ibrahim & Ithnin 2014) (Liu, Mehandjiev & Xu 2011)

(Lakiotaki & Matsatsinis 2011). These ones can

provide more information about the user

requirements.

To that extent, we need to build communities that

gather users who shared common interests. We

reason over these communities to derive similar

behaviours and provide recommendations. In this

light, clustering techniques have not only the goal to

gather users into several groups, but also to construct

communities to learn and to recommend latter similar

solution. Without any prior knowledge, we aim to

obtain overlapping clusters, and a single user would

belong to more than one cluster. With hard

classification, user is associated only to one cluster,

while the soft classification allows a user to join many

clusters with different degrees of membership. The

most known method is the fuzzy K-means algorithm.

Sometimes, it is combined with others data mining

algorithms such as ANN (Artificial Neural Networks)

presented in (Paireekreng & Wai Wong n.d.) in order

to classify users respect to their demographics data

and interests. The key drawback of this research study

is that it treated with very limited resources relating

to the mobile application (Paireekreng & Wai Wong

n.d.). (Lazzerini & Marcelloni 2007) have applied a

method gained on an unsupervised algorithm namely

the Fuzzy Divisive Hierarchical Clustering (UFDHC)

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

416

algorithm to classify the users of a web portal into

groups with similar characteristics and interests

represented by prototype. This research emphasizes

only the content of web pages related to specific

profiles. The Competitive Agglomeration for

Relational Data (CARD) algorithm is used in (Gandy

et al. 2005) to classify user’s session. (Castellano et

al. 2007)have focused on web personalization

problem and especially on discovering what current

webpage related to user profile. The clusters are

generated by mining the log data of a web containing

user’s preferences. Moreover, (Teran & Meier 2010)

have proposed a fuzzy recommender system for

election field. Their work had to identify the most

similar candidates according to voter’s preferences

and tendencies. They presented a modified fuzzy c-

means algorithm. A similar work, (Jalali et al. 2010)

have defined an architecture based on two phases to

predict the user future requests. The first phase is

turned on offline mode and it implemented the

clustering model for navigation pattern mining; it

consisted in computing the degree of connectivity

between each pair of the Web pages, and then created

an undirected graph to find the connect component.

The online phase has to predict the user future

intentions through mining Web server logs.

Table 1: Comparison between the fuzzy clustering

approaches.

Approaches Techniques Data type

(Paireekreng &

Wai Wong n.d.)

K-means and

ANN algorithm

Categorical

data

(Lazzerini &

Marcelloni 2007)

Fuzzy Divisive

Hierarchical

Clustering

(UFDHC)

characteristics

and interests

(Gandy et al.

2005)

CARD algorithm

Relational

Data

(Martin-Bautista

et al. 2002)

fuzzy

classification

rules

Categorical

data

(Teran & Meier

2010)

fuzzy c-means

algorithm

Categorical

data

(Jalali et al. 2010)

clustering model

with indirect

graph

Categorical

and numerical

data

The major challenge to consider with the use of fuzzy

clustering algorithm is to distinguish, as much as

possible, between the inter-clustering in order to

obtain a best differentiated subset, and to minimize as

much as possible the intra-class inertia, with the aim

of obtaining the most homogeneous possible clusters.

2.3 Motivation

There is no doubt that personalized systems have

been gaining interest in many domains and especially

in the transport field. Despite the various methods and

techniques presented in the literature, these

approaches have weaknesses and limitations. The

response must not only reply to user’s request, but

also it must anticipate his expectations before even he

expresses them. In this setting, we intend to explore

the histories of users to, one hand learns from their

experiences and on the other hand, to handle with the

problem of explicit or missed information. To this

end, the techniques of machine leaning seem be the

most appropriate to resolve this issue. We aim then to

gather users who have common interests to learn from

their experience. For this purpose, we opt for using

the technique of fuzzy clustering and we propose a

new dissimilarity measure to tackle the problem of

heterogeneous data.

3 GENERAL PROPOSED

FRAMEWORK

In this section, we describe the general proposed

framework for enhancing personalized research. The

aim of this proposal is to recommend for a current

user the most similar response to his request. In this

light, we split our proposal into two parts; the first

concerns the modelling phase and the second part

aims to respond to the user’s request according to our

recommender based model.

The figure 1 describes the input and the output of

the proposed reasoning process.

Figure 1: Reasoning process.

A Recommendation System for Enhancing the Personalized Search Itineraries in the Public Transportation Domain

417

3.1 User Profile Modelling

This section presents the characteristics of the user

profile ontology. The ontology was implemented

using Protégé beta 5.0 [25] in OWL DL and its

consistency tested using Jena and OWL Reasoner.

The user profile ontology was created in order to

facilitate the extraction of the user personal

information, needs, and interests, under the context of

personalization.

In this light, the user profile is defined as the

union of:









∪



∪



∪



With:

: represents his personal data (age, gender,

address, ability, proficiency, user’s state).

: depicts the user’s preferences for a precise

service. The preferences may be related to the travel

(cost, duration, walking, correspondence) or to the

user’s personal requirements (accommodation,

administration, entertainment and healthcare).

: defines the users’ histories (past query and

validated choice).

: defines contextual elements related to the user

such as time and location.

Figure 2: Top-level hierarchy of Ontology User Profile.

The figure 3 describes the main concepts of this

ontology, the properties (represent the attributes) and

the objects properties (used to establish relationships

between classes)

Figure 3: Classes, dataproperties, objectproperties,

description.

3.2 Reasoning based Process

In this section, we have many challenges to

overcome; First, unlike others approaches which used

a few criteria to respond to user’s request, we aim in

this paper to compensate different types of criteria to

obtain a personalized solution (itinerary). Further,

these criteria are generally treated as numerical data,

whereas it can be expressed in different forms. Even

more, users’ profiles are also different, and results

change from a profile to another. In this view, we

build our learning process. Accordingly, we propose

the fuzzy k-mode algorithm (Huang & K. Ng 1999)

within the inference engine to deal with the issues

mentioned above.

3.2.1 Initialization of Clustering Parameters

We symbolize then the terminologies used in our

clustering algorithm:

X,Y are two different users, 





,



,



,….,



represent the classes related to

user profile, xi={ x

,..,x

}: instances related to the

first user and y

={y

,…,y

} are the instances

related to the second user, Cluster: is a group of users

who share common characteristics (history, profile,

context and preference), Reference Vector (V

ref

)represents the various concepts used in the input of

our algorithm, Vector mode is specific to each cluster

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

418

and composed of the instances related to the concepts

of reference vector, Dissimilarity measure represents

the degree of closeness between two individuals,

Concept criterion( C

crit

)represents the concept topic

in each cluster, Concept non-criterion (C

Ncrit

)

represents the other concepts which may influence the

clustering process, Fuzzy index α, and finally, K is

the number of the cluster. The fuzzy clustering

algorithm is executed on 4 four step: 1) Selection

modes, 2) Computing of dissimilarity measure, 3)

Computing of membership matrix and 4) Upgrading

clusters. In the next section, we outline the different

steps of this algorithm.

3.2.2 Selection Vector Mode

The most straightforward approach is to choose

arbitrarily k random modes, but it turns out that it is

the weakest point of the conventional clustering

algorithm; the fuzzy k-mode algorithm as an example

(Choia & Chungb 2017). In addition, the correct

choice of k is often ambiguous. For this purpose,

several research efforts investigated the issue to more

wisely choose this parameter as discussed in

(Kodinariya & Makwana 2013). For the initialization

process, we choose k according to the theorem of rule

of thumb which is also similar to heuristic rule

(Madhulatha 2012). Thereupon, k is calculated as

following (n is the number of users) Eq.1



√

/2

(1)

First, the reference vector is composed of classes

from user profile. The choice of vector mode depends

on this vector, and it defines the instances related to

each class. For this purpose, we calculate the

frequency of instances using the following Eq2.

(xi)=







(2)

With n

: is the number of users that have the same

instance in the cluster and N is the total number of

users.

The selection of vector modes has two main

conditions; first, the instances of each vector mode

must not be duplicated in the other one and second, in

case of missing values, each empty instance must

follow our proposition of normalization method

explained in detail later with Eq.4

3.2.3 New Dissimilarity Measure

In order to deal with different type of data, we

propose a new dissimilarity measure. Further, the

comparison between each two instances (xi; yi) is

http://www.tfidf.com/

calculated based on their similarities and the type of

their related concepts (Ci (xi); Ci (yi)). We consider

for two individuals X and Y, the same reference

vector. Therefore, as we have different types of data

(numeric, non-numeric (textual and semantic)),

We assume that the concept criteria and non-

criteria are determined according to their weight in

the database as discussed in the next paragraph.

Forthwith, we propose four discussed cases:

 Case1: if (Ci (xi); Ci (yi)) ∈ C

crit

and xi = yi  xi

and yi are two similar instances of the same

concept (non-numeric). Their related concept is

considered as a concept criterion. Example: Ci (xi)

= Ci (yi) = "FamilyStatus"; xi = yi = "single".

 Case2: if (Ci (xi), Ci (yi)) ∈C

Ncrit

and xi # yi  xi

and yi are two similar instances of the same

concept but this concept does not belong to the

concept criterion. Example: Ci (xi) = Ci (yi) =

"gender"; xi = "female" and yi = "male".

 Case3: if (Ci (xi); Ci (yi)) ∈{C

crit

; C

Ncrit

} and (xi;

yi) are numeric values  xi and yi belong to the

same concept. Example: Ci (xi) = Ci (yi) = "age";

xi = "13" and yi = "20".

 Case4: if (Ci (xi); Ci (yi)) ∈ C

crit

and  xi and yi

are two different instances but belong to the same

concept. This latter belongs to the concept

criterion. Example: Ci (xi) = Ci (yi) =

"PreferenceTravel"; xi = "fast" and yi = "cheaper".

The enhanced similarity measure is then:









,















































,











∈









∶1

















,











∈









:2













,





∈











,











∈







,





:3































,











∈









∶4

0











(3)

The similarity is calculated between two users’

vector. Therefore, for case1 and case 2, we opt for

using Jaccard similarity (Jeff M. 2013) for computing

similarities between two strings or textual attributes,

each attributes is associated to a weight. For this, we

use the technique of TF-IDF

. With this method, we

can define which concept (class) is a concept

criterion. Each instance is associated with a weight w

to evaluate how important this instances in database.

For case3, we compute the similarity using the

method proposed in (Bouhana et al. 2013) for

numerical attributes. Afterwards, OWA aggregator

(Yager 1988) is applied to compute the average

similarity. The weights are generated automatically

according to the orness measure and dispersion

measure. The case 4 discusses uncertain criteria. For

this propose, a

WordNet

is applied to search the

synonyms, the hypernym, the hyponym and any

https://wordnet.princeton.edu/

A Recommendation System for Enhancing the Personalized Search Itineraries in the Public Transportation Domain

419

existing relation between the attributes of each users.

However, in order to deal with missing information,

a normalization technique is adopted. We assign a

value (ε) for each empty attribute, we have then:







0 









1 







(4)

As a result, the distance D is defined then as the ratio

of the sum of similarities measures between the

attributes related to each individual and the sum of θ

The new formulation is given below:







,





∑





,









∑









(5)

3.2.4 Membership Matrix Upgrading

The next step of our fuzzy clustering algorithm is to

calculate and update the membership matrix

[



]. The membership matrix allows the degree

of user closeness with its corresponding cluster to be

identified. This value is to be updated as far as we do

not hit the stop condition.

The formula is expressed as follows:

















1 







0







1

∑





































 















(6)

Where

: is the set of user’s attributes, C

: represents the

current mode concepts of a cluster which a user

belongs to and C

: represents the concepts of others

modes which a user does not belong to.

3.2.5 User’s Clustering Upgrading

The mode related to each cluster is not static since the

algorithm did not reach the stop condition. To update

the mode, we calculate the following formula:







∑





∗







∑













(7)

Where:



is the membership matrix for the ith iteration α is

the fuzziness index and X

is the instances of user X.

This step continuous and the new mode is compared

with the previous one until attending the stop

condition. The stop condition is obtained if and only

if the following objective function is minimized:

m



,





∑∑























,





(8)

3.2.6 Recommendation Process

Hereinafter, we describe the execution of our

recommendation process; the algorithm is described

as follows:

Input

Mod

: initial vector mode

X={x

, x

,…., x

}: instances of user X

 = fuzzifier index

N=number of clusters

t =1 // number of iterations



: Membership matrix



: Vector mode upgraded

Output

Clusters= {CL





,CL





,CL





,…,CL





} //set of

clusters

Begin

//Initialize the fuzzifier

index 

N=Numbers_of_concepts_criterion

//Construction of modes

Foreach CL





in clusters do

Foreach C

crit

(x)

Begin

1) Initialize the vector mode

(see Eq.(1 and 2))

2) Calculate new similarity

measure (see Eq.(3))

3) Calculate new dissimilarity

measure 









,





(see

Eq.(5))

If









,





is the less one then

X∈Cluster

// save user

profile in cluster

End If

4) Calculate ω



based on Z



with

Eq.(6)

t++

5) Calculate



based

on



withEq.(7)

6) //Verify objective function

(seeEq.(8))

If (



,



=F

(



,



thenStop.

Else

Go step (7)

End if

7) Determine



based on 



If F (



,



=F

(



,



thenStop.

Else Go step (4)

End if

8) Repeat Step (4) to step

(7), until there is no

movement between clusters

(minimize the objective

function)

End

Return similar user in

current cluster

Return recommended solution

End

ICEIS 2017 - 19th International Conference on Enterprise Information Systems

420

4 EXPERIMENTAL

EVALUATION

In this section, we focus on the following three

questions. One is to check whether such initialled

parameters (fuzziness index and number of clusters)

negatively affect the quality of resulting clusters

(final solution). The other is to experimentally

evaluate how our clustering algorithm is in agreement

with other alternative algorithms and finally, we

evaluate the proposed solution by addressing the

following research question. How accurately the

proposed approach can provide pertinent solution?

This is addressed by calculating the precision and the

recall. For this setting, a case study is presented in the

next section.

4.1 Case Study

Personalized information systems in intelligent

public transportation domain are complex systems

and dealing with a large amount of heterogeneous

data from various sources. These systems must

respond to user’s queries based on their requirements

and also profiles. In fact, in traditional systems, user

should provide all needed information explicitly to

get a personalized response. However, user interacts

with the system in order to search a personalized

itinerary even if he doesn’t dispose of all the travel’s

information; modality, time-tables and tariffs. In this

setting, we recommend for him an itinerary in a

multimodal network based on the deducted

community of similar users. For the instance, we are

interested only on the recommendation process. We

consider a community of 14 users, every user

executes 3 different queries, and we have at least 42

solutions. Based on the inferred knowledge provided

by this community, we will recommend for the

current user the most suitable itinerary.

To this end, ontology of the domain that describes

the field of transport has been exploited and enriched

by RATP OPEN DATA

and GTFS

. The figure 4

describes an extract of this ontology

https://opendata.paris.fr/explore/

Figure 4: Classes, dataproperties, objectproperties,

description.

4.2 Evaluation Results and Discussion

We have implemented our method in Java language

and executed in Intel core i3 processor (1.9GHz) with

2GB memory running on windows 7 operating

system.

 Clustering Quality

In order to justify the quality of clustering results, we

use the Cohen’s kappa value, which is widely used

especially to measure the agreement between the

proposed algorithm and the other competitors. The

values between -1 and 1 assert that the accord is low,

or very low, moderate agreement or strong

agreement. The formula applied with Kappa value is:











1



(9)

With P

: is the relative observed agreement among

ratters and P

is the hypothetical probability of chance

agreement, using the observed data to calculate the

probabilities of each observer randomly saying each

category.

Table 2: Cohen’s Kappa.

Results

New fuzzy

K-modes

Clustering

Fuzzy

K-means

clustering

K-modes

clustering

New fuzzy

K-modes Clustering

0,67

0,71

Fuzzy

K-means clustering

0,67

1 0,4

K-modes clustering 0,71 0,4 1

https://developers.google.com/transit/gtfs/reference/

A Recommendation System for Enhancing the Personalized Search Itineraries in the Public Transportation Domain

421

As shown in this table, the Kappa value is calculated

for each pair of clustering techniques used. The new

fuzzy K-modes clustering and the fuzzy K-means

have good agreement strength of 0,67 (0,67∈

0,6;0,80, while new Fuzzy K-modes and K-modes

clustering have an agreement strength of 0,71.

We admit that our proposed algorithm seems to

show good results.

 Precision, Recall and F-measure

The Precision is defined by dividing the number of

users correctly belonging to the positive cluster by the

total number of users belonging to the positive cluster

while Recall is calculated as the ratio between the

number of users correctly to the positive cluster and

the total number of elements that actually belong to

the positive cluster. Indeed, the results is a set of

clusters, we evaluate these measures by calculating

the average of the precision and the recall in each

clusters. Finally, we compute the F-measure for each

query.







,





,2∗

∗





Table 3: Evaluation results.

Query

Estimated

solution

Recom

mended

Solution

Precision Recall

F-

measure

S8, S7,

S27, S38

1) S12, S27,

S42 2) S8,

S15, S23,

S30, S38

3) S14

0,33 0,75 0,46

S28, S13,

S33

1) S13, S33,

S28 2) S11,

S26, S35,

S37 3) S6,

S21

0,33 1 0,50

S8, S28, S1,

S30

1) S8, S15 2)

S5 3) S13,

S18 4) S20,

S23, S30,

S38

0,22 0,5 0,31



Figure 5: Precision, Recall and F-measure evolutions.

The found results presented in Fig. 5 shows that our

algorithm returned most of the relevant results

according to the high recall's values.

5 CONCLUSIONS

In this paper, we propose a new recommendation

system based on fuzzy clustering algorithm jointly the

ontologies in the public transportation field. The

proposal supports both qualitative and quantitative

data and aims to gather users who share common

features into the same cluster. By building such

clusters, called communities, we raise the problem of

explicit information by learning from the similar

profiles according to their interactions’ histories with

the system. The recommender solution fit his needs

and responds to his formulated request. In our future

work, we will detail how we reformulate the query

sending by the user by adding the new implicit

information inferred from the proposed clustering and

we manage to use BabelNet to overcome the limits of

the WordNet.

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