Semantic Collaborative Filtering for Learning Objects

Recommendation

Lamia Berkani

1, 2

and Omar Nouali

Departement of Computer Science, USTHB University, Bab-Ezzouar, Algiers, Algeria

Higher National School of Computer Science (ESI, ex. INI), Oued Smar, Algiers, Algeria

Department of Research Computing, CERIST, Algiers, Algeria

Keywords: Online Communities of Practice, Learning Resources, Recommendation System, Ontologies, Semantic

Filtering, Collaborative Filtering.

Abstract: The present paper proposes a personalized recommendation approach of learning objects (LOs) within an

online Community of Practice (CoP). Three strategies of recommendation have been proposed: (1) a

semantic filtering (SemF) by member’s interests; (2) a collaborative filtering (CF) based on the member’s

expertise level; and (3) a semantic collaborative filtering combining in different ways the two approaches.

The expertise level of a member is calculated in relation to all of his domains of expertise using the domain

knowledge ontology (DKOnto). A similarity measure is proposed based on a set of rules which cover all the

possible cases for the relative positions of two domains in DKOnto. In order to illustrate our work, some

preliminary results of experimentation have been presented.

1 INTRODUCTION

The great expansion and explosive use of the

Internet has created new ways of collaboration

between people as well as exchange and sharing of

knowledge. A vast number of learning object

repositories are made available to any user searching

for educational content on various topics

(Tzikopoulos et al., 2007). However, one of the

main problems encountered actually is the selection

of the appropriate resources. Accordingly, to deal

with the problem of information overload, the need

for recommender systems is more than necessary.

The main objective of our research is to facilitate

access and reuse of knowledge within a CoP of

teachers. The main objective of this community is to

promote e-learning in higher education context

applied to the domain of computer science.

We propose in this paper a personnalized

recommendation approach of learning objects (LOs)

for members of this CoP, based on the semantic

collaborative information filtering approach. Three

strategies of recommendation have been proposed:

(1) a semantic filtering (SemF) by member’s

interests; (2) a collaborative filtering (CF) based on

the member’s expertise; and (3) a semantic

collaborative filtering combining in different ways

the two approaches. These strategies are based

respectively on the following member’s objective:

specialization ; learning; or both, specialization and

learning. The CF is used to predict the utility of LOs

for members based on the similarity among their

preferences and the preferences of other members.

The SemF is used, to take advantage of the enhanced

semantics representation.

The main contribution of this paper concerns: (1)

the proposition of a set of rules to calculate the

similarity between the domains of interests of the

member and each of the domains of the LO; and (2)

the proposition of a pseudo usage matrix for the

prediction of evaluations using the CF approach,

which is based, both, on the members’ evaluations

and on the members’ expertise levels and

importance degrees of the domains of the LOs.

The remainder of this paper is organized as

follows: Section 2 presents a litterature review about

recommendation systems and approaches in the

technology enhanced learning. Section 3 proposes a

personalized recommendation approach of LOs

within an online CoP. A prototype of the proposed

recommendation system and the experimental results

are presented in Section 4. Finally, the main

contribution and some future perspectives are

discussed in the conclusion.

Berkani L. and Nouali O..

Semantic Collaborative Filtering for Learning Objects Recommendation.

DOI: 10.5220/0004550500520063

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference on Knowledge

Management and Information Sharing (KDIR-2013), pages 52-63

ISBN: 978-989-8565-75-4

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

2 LITTERATURE REVIEW

Recommender systems aim to generate suggestions

about new items or to predict the utility of a specific

item for a particular user.

2.1 Recommendation Approaches

Three types of approaches are distinguished: (1) the

content-based filtering (CBF) recommenders, are

built on the assumption that a person likes items

with similar features to those of other items he

preferred in the past (Peis et al., 2008); (2) the CF

recommenders, generates suggestions about data

items that users with similar tastes and preferences

liked in the past (Shafer et al., 2007); and (3) the

hybrid recommenders try to overcome the

shortcomings of the two previous approaches by

combining them in different methods (Burke, 2007).

With the advent of the semantic web, a new

generation of recommender systems based on

ontologies has emerged. These approaches take

advantage of the enhanced semantics representation.

2.2 Related Work

The state of the art shows a large number of

recommendation systems proposed in the context of

formal education, i.e. including learning offered

from educational institutions (e.g. universities,

schools) (Manouselis et al., 2009). A discussion of

the advantages and limitations of different

techniques applied in this context was presented in

(Drachsler et al., 2008). However, few works have

been proposed in an informal setting (Ziovas et al.,

2010).

As reported by Manouselis et al. (2009) an

informal setting is described in the literature as a

learning phase of so-called lifelong learners who are

not participating in any formal learning and are

responsible for their own learning pace and path

(Colley et al., 2002; Longworth, 2003). Online

communities and social networks are examples of

such contexts.

We mention for example the following

recommendation systems proposed in an informal

setting: (1) the QSIA system (Questions Sharing and

Interactive Assignments) to share educational

resources, evaluation and recommendation in the

context of online communities (Rafaeli et al., 2004).

(2) The ReMashed system for learners in informal

learning network (Drachsler et al., 2009). The main

objective of this system is to offer personalized

recommendations from the emerging information

space of a community.

The review of the literature shows that most

systems provide resources (Tang and Mccalla, 2003)

and / or individuals (Recker and Wiley, 2003),

which can help in a learning activity. Other systems

recommend courses, offering some advices to

learners for their registration in training sessions

(Garcia-Molina, 2008), or appropriate activities and

their execution sequences, allowing learners the

selection of the appropriate activities to achieve

some educational objectives (Hummel et al., 2007).

The lack of work in an informal education

motivated us to apply this approach in the context of

online CoPs. Our goal is to propose a personalized

recommendation approach taking into account the

advantages of existing hybrid systems, especially in

the domain of e-learning which is very close to our

context of study.

The proposition of a recommendation approach

in CoPs is necessary because existing systems in e-

learning, for example, can not be used directly in the

community. Learning is informal, participation

being unsupervised and the objectives and

constraints are different. In our case, the

personalization will take into account other

parameters linked, for example, to member's

expertise, skills, purpose, etc. In addition, the

representation of the resource will also take into

account the evaluation aspect according to several

dimensions: feedback, results, analysis, etc. We will

focus in this paper on the members’ profile, taking

into account some specific dimensions that are

important in the context of a CoP such as the

member’s objective, his interests and expertise.

3 A RECOMMENDATION

SYSTEM FOR COPS

We propose in this section a personalized

recommendation system for CoPs of teachers.

3.1 Recommendation Strategies

As illustrated in the Figure 1, three recommendation

strategies are proposed, according to the member’s

objective:

1. Strategy 1: If the objective is a “Specialization”,

then the system applies a SemF by domains of

interests.

2. Strategy 2: If the objective is a “Learning”, then if

there are enough ratings the system applies a CF,

language). Similarly, if the member prefers textual

resources, then the system will remove the

multimedia resources, etc.

The recommended resources will be assigned

with priorities taking into account different

parameters, such as: the difficulty of the resource

and the expertise degree of the member; see if the

resource has been visited or not, evaluated or not

(i.e. resources that are not yet visited have more

priority).

3.3 Collaborative Filtering

In the context of a CoP, members have different

levels of expertise. Accordingly, we consider that

the scores given to the resources based on the

evaluations of members should take into account this

difference of levels between members.

More formally, we propose to interpret the usage

matrix, V, taking into account the members’

expertise level for the evaluated LOs. The expertise

level of a member is calculated in relation to all of

his domains of expertise using the ontology DKOnto

(see Figure 2). We formulate the problem as follows:

Let M be a member and R a LO. Let M’s

domains of expertise be defined as a vector E

, E

, …, E

]. We associate with this vector a

vector D

= [d

, d

, …, d

] (with same size as E

)

meaning that M has a degree of expertise d

in the

domain E

, where 0 ≤ d

≤ 1. (It should be noted that

‘m’ can be strictly smaller than the total number of

domains in the ontology.) Each resource has a set of

relevant domains denoted by vector 











,





,…,





.

The idea behind this formalization of the

problem is that each domain of expertise E

of M is

similar to a certain degree to each domain 





of R.

Thus, we define the similarity matrix of M’s

expertise with respect to R as an (m x 



) matrix as

follows:

Similarity(M,R) =







,





…



,







⋮



,





⋮





,





…



,









(2)

where:

0 ≤ 



,





 ≤ 1 is the similarity between M’s

domain of expertise E

and the domain 





of R.

We know that each domain 





of R has a

weight 0 ≤ 





≤ 1 for this resource. Each member M

has a degree of expertise 0 ≤ d

≤ 1 with respect to

his domain of expertise E

. We define the degree of

expertise of M with respect to 





(domain j of R) as

follows:

Expertise(M,





∑









,











(3)

We define an overall degree of expertise of M

with respect to R as being:

Expertise(M,R)=

∑







,











(4)

We finally calculate the interpreted usage matrix, by

considering only the evaluations of members having

an expertise degree greater than or equal to 0.5 for

R, as follows:

′













,



0,5

0, 

(5)

The obtained matrix will be used in the two steps of

the CF: (1) to calculate the similarity between the

members and infer communities, and (2) predict

notes for resources and select only those with a high

score. The evaluation consists to give a score (1-5),

from very bad to very good. Accordingly, we chose

the Pearson similarity correlation, for the prediction

of the evaluations.

3.4 Hybrid Filtering

We have proposed different methods combining the

semantic and the CF approaches. We present in this

paper two algorithms of hybridization as follows:

3.4.1 A Semantic Boosted CF Approach

The main idea is to apply a SemF, then provide

suggestions through a CF. The SemF is applied to

each row of the matrix and gradually generates a

pseudo matrix, PV. Each row, i, of this matrix

includes the evaluations given by the member M

, if

they are available; otherwise the predictions

calculated using the SemF are considered:











,













,

(6)

where:

refers to the score given by the member M

on the

resource R

refers to the score calculated using the SemF.

The system applies a semantic recommendation

and then the similarity results are converted into a

set of scores from 1 to 5, as follows:

If Similarity (M

, R

)  [0, 0.2] then score =1

Elseif Similarity (M

, R

)  [0.2, 0.4] then score =2

Elseif Similarity (M

, R

)  [0.4, 0.6] then score =3

Elseif Similarity (M

, R

)  [0.6, 0.8] then score =4

Elseif Similarity (M

, R

)  [0.8, 1] then score =5

Finally, the CF is applied using the PV matrix.

3.4.2 A Feature Combination Approach

We propose an approach which combines the CF

and the SemF approaches using a distance formula.

The collaborative distance represents the correlation

between resources using the Pearson function, while

the semantic distance represents the similarity

between the resources using the "similarity rules",

we have proposed in section 3.2.3.

We adopt a combination method to enrich the

neighborhood, combining both semantic and

collaborative distances, using the following formula:

DistanceCo

‐

DistanceSe

‐

Distance/

7

where:

Col-Distance refers to the collaborative distance,

Sem-Distance refers to the semantic distance,

Distance represents the distance between the

resources.

The recommendation will be based on the value

of a predefined threshold, t. A set of resources will

be suggested to the member where the value of

“Distance” is greater then or equal to “t”.

4 RESULTS AND EVALUATION

4.1 ReCoPSyst: A Prototype

of a Recommendation System

In order to illustrate our work, we have developed a

personalized recommendation system called

“ReCoPSyst”, based on the proposed approach. In

order to evaluate this system, we considered a CoP

called CoPHEduc (CoP Higher Education), made up

of actors who are interested to teaching in computer

science in the university.

Figure 6 shows a screenshot of the proposed

recommendation system for this community. The

prototype ReCoPSyst was included in the

CoPHEduc portal. We can see the personalized

space of the member M1, offering for instance the

following functionalities:

 Personalized recommendation of LOs and

members.

 Last visited LOs.

 Notifications about new added members, new

LOs, etc.

ReCoPSyst offers different recommendation

services based on the proposed approaches:

 A Semantic recommendation service based on

the similarity measures. Furthermore, we have

developed other similarity recommendation

services using some existing metrics such as

Wu and Palmer (1994).

 A collaborative recommendation services

using different similarity functions (Pearson,

cosine...) and according to different

recommendation types (user-user or item-

item).

 Hybrid recommendation services using the

above mentioned algorithms (e.g. a semantic

boosted collaborative approach and a feature

combination approach).

We can see in Figure 7 an example of a

collaborative recommendation service. The member

can see the description of each recommended

resource, download or evaluate it. Furthermore, the

system proposes additional information about the

evaluations made by other members for each

resource (e.g. the average resource assessment, the

number of evaluators).

4.2 Tests and Evaluation

We present in this section the results of two

experimentations: (1) a qualitative evaluation; and

(2) an offline evaluation, using some existing

datasets.

4.2.1 Qualitative Evaluation

An experimental study was conducted to explore the

benefits of using the recommender system within

CoPHEduc. We describe in this section the results of

an investigation we have made to evaluate

ReCoPSyst prototype. Fifteen teachers from the

community were asked to use ReCoPSyst and then

each one provided us with a detailed feedback of

use. We have gathered more than 350 resources

from different websites such as Amazon.

Furthermore more than 300 resources were captured

by members using the system. The resources are

related to some domains of our DKOnto. The

distribution of the domains of relevance of resources

and domains of interests of members by the selected

domains is described in the table 2 below. Figure 8

illustrates this distribution, given that each resource

may be linked to several domains, and similarly,

each member may have many domains of interests.

The questionnaire of evaluation, we have

proposed, includes ten questions using a five-point

Likert scale (SA, strongly agree; A, agree; U,

undecided; D, disagree; SD, strongly disagree). The

questions are classified under four dimensions: (1)

usability, in terms of facility of use and quality of

presentation; (2) effectiveness, in terms of pertinence

such as Amazon, Book Crowsing and Merlot. The

main objective by this evaluation is to identify

which strategy is more suitable in the context of a

community of practice of teachers. Furthermore, it

will be necessary to validate the recommender

system comparing its effectiveness with other

systems on the topic such as the recommender

system for CiteSeer (Kodakateri et al., 2009) or the

recommender system proposed by Cobos et al.

(2013).

In addition, in order to improve the response

time of the proposed recommendation services, it

will be interesting to enrich our approaches using the

classification techniques.

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