Exploiting Linked Data-based Personalization Strategies for

Recommender Systems

Gabriela Oliveira Mota Da Silva

1,2 a

, Lara Sant’Anna do Nascimento

1 b

and Frederico Ara

ujo Dur

1 c

Institute of Computer Science, Federal University of Bahia, Avenida Milton Santos, s/n, Salvador, Brazil

Federal Institute of Education, Science and Technology of Bahia, Avenida Centen

ario, 500, Jacobina, Brazil

Keywords:

Recommender Systems, Linked Data, Personalization, Semantic Similarity, Feature Selection.

Abstract:

People seek assertive and reliable recommendations to support their daily decision-making tasks. To this end,

recommendation systems rely on personalized user models to suggest items to a user. Linked Data-driven

systems are a kind of Web Intelligent systems, which leverage the semantics of links between resources in the

RDF graph to provide metadata (properties) for the user modeling process. One problem with this approach

is the sparsity of the user-item matrix, caused by the many different properties of an item. However, feature

selection techniques have been applied to minimize the problem. In this paper, we perform a feature selection

preprocessing step based on the ontology summary data. Additionally, we combine a personalization strategy

that associates weights with relevant properties according to the user’s previous interactions with the system.

These strategies together aim to improve the performance and accuracy of the recommender system, since

only latent representations are processed by the recommendation engine. We perform several experiments on

two publicly available datasets in the ﬁlm and music domains. We compare the advantages and disadvantages

of the proposed strategies with non-personalized and non-preprocessed approaches. The experiments show

signiﬁcant increases in top-n recommendation tasks in Precision@K (K=5, 10), Map, and NDCG.

1 INTRODUCTION

Asking for recommendations is part of everyday life

for many people in different areas, whether it is shop-

ping, reading a book, listening to a song, or even look-

ing for a romantic couple. Thus, one searches for a

reliable source of recommendation so that the object

sought is following their tastes (Ricci et al., 2015). To

this end, recommender systems (RS) increasingly rely

on personalized user models by exploiting metadata

from the items that a user has previously interacted

with to suggest similar items they might be interested

in. The user model plays a signiﬁcant role in many

personalized applications. For example, an RS ana-

lyzes the 1 to 5-star ratings given by a user to movies

and music in the past to establish their general inter-

ests in art. The system then evaluates and suggests

other items that it expects the user will genuinely like

in the future.

https://orcid.org/0000-0001-8799-5642

https://orcid.org/0000-0001-7428-9992

https://orcid.org/0000-0002-7766-6666

Linked Data (LD) or Linked Open Data (LOD)

is a powerful source of diverse data structured under

open Semantic Web standards, such as Resource De-

scription Framework (RDF) and SPARQL (Berners-

Lee, 2009). DBpedia

is a notable example of a LOD

set built by a strong community effort to extract struc-

tured information from Wikipedia and make this in-

formation openly available on the Web.

In the past years, LD datasets such as DBpedia

have been proposed as a valuable source of informa-

tion to increase the predictive power of RSs (Di Noia

et al., 2018). In such Linked Data-driven RS, links

between resources in the RDF graph play the primary

role of providing metadata (properties or features) for

the user modeling process and the recommendation

model (Passant, 2010; Di Noia et al., 2012; Piao and

Breslin, 2016). These systems use a semantic simi-

larity algorithm that calculates the degree of match-

ing between pairs of Linked Data resources. Because

RDF represents data as a graph, these algorithms, in

general, count the number of direct and indirect links,

https://www.dbpedia.org/

226

Da Silva, G., do Nascimento, L. and Durão, F.

Exploiting Linked Data-based Personalization Strategies for Recommender Systems.

DOI: 10.5220/0011591300003318

In Proceedings of the 18th International Conference on Web Information Systems and Technologies (WEBIST 2022), pages 226-237

ISBN: 978-989-758-613-2; ISSN: 2184-3252

the length of the path between two resources, or their

place in the hierarchy of classes (Passant, 2010; Piao

et al., 2016; Cheniki et al., 2016).

One problem of this approach is the sparsity of

the user-item matrix, provided by the numerous dif-

ferent properties one item could have. However, vari-

ous feature selection techniques have been applied to

discard irrelevant properties and minimize the prob-

lem. These techniques ﬁlter properties that can cause

the system’s reliability to decrease and only consider

the most relevant subset of the original data in the rec-

ommendation process step.

Nevertheless, some effort has been made to select

subsets of features that seem more helpful in com-

puting similarity between items of a graph dataset

(Catherine and Cohen, 2016; Musto et al., 2016),

reducing the matrix dimension. Recent studies cite

several methods to ﬁnd and exclude or rank features

based on diverse goals — see Section 4 for examples.

This work investigates LD-based personalization

strategies to provide more representativeness to the

user model. The proposed algorithm associates

weights to the properties of items, according to their

relevance to the user. The relevance is calculated by

identifying among the items that the user has pre-

viously liked (user model), which properties link to

the same item repeatedly. We assume that properties

that appear more often in a particular user’s model

are more important for the calculation of future rec-

ommendations. We also perform a ﬁltering prepro-

cessing step in which we apply an ontology-based

summarization method (Di Noia et al., 2018) to ﬁl-

ter properties according to the domains. In summary,

these strategies together aim to reduce the dimension-

ality of the spaces and enhance the recommender sys-

tem performance.

We perform several rounds of experiments with

two different domain datasets - MovieLens and

LastFM, enriched with DBpedia semantic informa-

tion, to evaluate the system’s effectiveness and tune

its parameters. The similarities are calculated with

the Personalized Linked Data Semantic Distance

(PLDSD) method (da Silva et al., 2019). We present

the advantages and disadvantages of the proposed

strategies by comparison with non-personalized and

non-preprocessed approaches and discuss the results

in terms of performance and accuracy.

The remainder of the paper is organized as fol-

lows: Section 2 explains about Linked Data driven

RS; Section 3 discusses feature selection ad other per-

sonalization methods; Section 4 lists the work related

to this research; Section 5 introduces and describes

the proposed personalization approaches; Section 6

sets forth the experimental evaluation in the context

of two LD-based recommender systems simulation;

Section 7 includes the discussion on the results and

some points of improvement; and Section 8 concludes

the paper and provides suggestions for future work.

2 LD-BASED RECOMMENDER

SYSTEMS

Recommender Systems (RSs) are software tools and

techniques that aim to solve the information overload

problem by suggesting items that are most likely of

interest to a particular user (Ricci et al., 2015). Lit-

erature essentially discusses two recommender meth-

ods: Content-Based (CB), in which the similarity of

items is calculated by analyzing features associated

with the compared items; and Collaborative Filter-

ing (CF), in which recommendations to one user are

based on items that other users with similar tastes

liked in the past. Regardless of the chosen technique,

the user model plays a central role in recommender

systems since it encodes the user’s preferences and

needs (Ricci et al., 2015). For instance, in consider-

ing a CB approach, the user can be proﬁled directly

by its ratings of items. Thus, in a LD enriched Rec-

ommender System context, resources and their rela-

tions are mixed to the user model (ratings over items)

to calculate similarity and generate personalized user

recommendations (da Silva et al., 2019).

The data that describe items in an RS are derived

from many sources, most often private databases be-

longing to companies. However, the availability of

open sources of knowledge is growing, increasing the

emergence of semantic-based applications, for exam-

ple, Semantic-Aware SRs (de Gemmis et al., 2015).

Linked Open Data (LOD) is a widely known project

that aims to connect open datasets over the Web,

and facilitate their use by applications (Bizer et al.,

2009). The LOD project consists of 1,255 datasets

with 16,174 links, as of May 2020

, that covers an

extensive collection of statements related to resources

— such as people, places, songs, movies and so on. A

typical LOD dataset is DBpedia, which incorporates

links to other datasets, such as DailyMed, Geonames,

and LinkedGeoData. As these additional links are

provided, applications can exploit knowledge from

interconnected datasets for developing semantic ap-

plications.

Semantics-aware applications rely on an RDF

graph architecture. The RDF statement (also

known as triple) has the form of subject-predicate-

object, each part uniquely identiﬁable by Uni-

https://lod-cloud.net/

Exploiting Linked Data-based Personalization Strategies for Recommender Systems

227

Figure 1: A DBpedia movie context example.

form Resource Identiﬁers (URIs). For example,

in the DBpedia movie segment shown in Figure

1, dbr:The Avengers is the subject of one triple,

dbo:director is the predicate, and dbr:Joss Whedon

is the object, forming the sentence: The Avengers

was directed by Joss Whedon. Another triple links

the subject dbr:Alien Ressurection to the same ob-

ject dbr:Joss Whedon by a different property —

dbo:writer — forming, in turn, the sentence: Alien

Ressurection was written by Joss Whedon.

This work focuses on CB Recommender Systems,

which compute the similarity between items in the

system by comparing their characteristics (also called

features). For instance, in a movie Recommender

System, algorithms reason about the degree of sim-

ilarity between two movies by comparing their direc-

tors, actors, the main subject, and so on. Although

several studies address different ways to build recom-

mendations, RS researchers still discuss some prob-

lems. One is the sparsity problem, when there is

much less feedback data in comparison to the whole

data matrix size; or the well-known cold-start prob-

lem, that happens when a new item or a new user is

added to the system. Another common issue is how

to offer more personalized recommendations to users.

LD are often used to add semantics to the system in

order to help address those well-known RS problems

(Di Noia et al., 2012; de Gemmis et al., 2015; Joseph

and Jiang, 2019).

In LD-based RSs, items’ features are expressed

by links between nodes in the RDF graph, that rep-

resent the resources’ properties. Thus, similarity al-

gorithms need to consider the semantics brought by

those links. Different similarity measures were pro-

posed to handle this task through LD-based systems

(Passant, 2010; Di Noia et al., 2012; Piao and Breslin,

2016). Most of them can manage the sparsity prob-

lem well. Nevertheless, one typical characteristic of

the previous work on LD similarity measures is that

most research considers all the links on an RDF graph

as having equal importance. Retaking the movie RS

context, genre is considered an important feature to

the movie domain. One user although likes to watch

only to recent released titles. Given this hypotheti-

cal scenario, is it correct that algorithms consider the

property genre as having the exact weight of the re-

lease year of a movie? Based on this issue, we will

discuss some ways to rank features regarding a given

RS context and/or select the best combination of prop-

erties that ﬁts a particular user model.

3 FEATURE SELECTION AND

PERSONALIZATION

Many machine learning tasks, such as recommender

systems, demand a preprocessing step to ﬁlter a

smaller and more signiﬁcant portion of the dataset.

When working with Linked Data, a single item in the

original dataset may have hundreds or thousands of

variables (as many as the number of links), causing

the feature matrix to be oversized. Studies covering

this issue focus mainly on two branches of research:

selecting subsets of features that are useful to build a

good predictor — Feature Selection (FS), and rank-

ing all potentially relevant variables in a context —

Feature Ranking (FR) (Guyon and Elisseeff, 2003).

One common way of performing FS tasks is

through hand-crafted work: manually selecting the

most relevant features according to simple heuristics

or domain knowledge. Nevertheless, there are at-

tempts to automatize the process, for example, Musto

et al. (Musto et al., 2016) assess the impact of some

classic feature selection techniques on recommenda-

tions accuracy, such as Principal Component Analysis

(PCA), Information Gain Ratio (GR), PageRank (PR)

and Support Vector Machines (SVM).

In an LD-Based Recommender System, proper-

ties are considered as features of a given node in the

knowledge graph. In a different approach, Di Noia et

al. (Di Noia et al., 2018) show how LD-based sum-

marization can drive FS and FR tasks by comparing

an automated feature selection method based on on-

tology data summaries with other classical ones, like

manual selection.

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

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Although FS and FR preprocessing techniques are

commonly applied in recommended models, most of

them consider links as having equal importance from

the user’s point of view. In this work, we further per-

form an FR task personalized. We not only use on-

tology summarization to select the most relevant fea-

tures from the LD graph, but we rank them by adding

weights to each link according to the user model.

This step is called graph personalization and is re-

sponsible for automatically ranking the properties that

most inﬂuenced the ratings given by a user of a rec-

ommender system. For example, in a movie RS, the

genre, artists, and director properties are usually con-

sidered important by automated FS algorithms such

as ontology summarization. However, if a particular

user has given positive ratings to movies of various

genres and directors, but most are short movies, our

personalization method will classify the movie length

as more important than the other properties, giving it

a higher weight.

4 RELATED WORK

This work addresses the problem of building a recom-

mender system over a LOD dataset, where resources’

properties form sparse matrices. Literature is plenty

of work that draws on a feature-based deﬁnition of

Linked Data, such as Meymandpour & Davis (Mey-

mandpour and Davis, 2016) which proposes a gener-

alized information content-based approach with sys-

tematic assessment of semantic similarity between

entities. They extensively reviewed existing seman-

tic similarity measures and proposed a hybrid method

made of feature-based and statistical techniques.

One example of a semantics-aware application

is when a Knowledge Graph (KG) is used to pro-

vide better user recommendations (Catherine and Co-

hen, 2016). In most works following this line of re-

search, the authors enrich benchmark databases with

semantics given from the interconnections between

KG nodes. Musto et al. (Musto et al., 2016) studies

the impact of using knowledge coming from Linked

Data on the overall performance of a recommenda-

tion algorithm. They reduced the matrices dimension

by testing several feature selection techniques against

two datasets. They investigate whether the integra-

tion of LD-based features improves the effectiveness

of the algorithm and to what extent the choice of dif-

ferent feature selection techniques inﬂuences its per-

formance in terms of accuracy and diversity.

Furthermore, Di Noia et al. (Di Noia et al., 2018)

show how LD-based summarization can drive fea-

ture selection tasks by proposing a fully automated

method based on semantics provided by the ontol-

ogy. They evaluate the accuracy of each strategy used

and compare them to classic methods such as man-

ual selection and statistical distribution methods. Fi-

nally, they aggregate diversity to the recommender

system by exploiting the top-k selected features. In

the inverse direction, Van Rossum & Frasincar (van

Rossum and Frasincar, 2019) investigate the incorpo-

ration of graph-based features into path-based simi-

larities. They proposed two normalization procedures

that adjust user-item path counts by the degree of cen-

trality of the nodes connecting them. It’s based on the

idea that a user liking one movie tells us more about

the popularity of this movie than about the particular

user. Our approach has similar premises, however, the

methodology diverges as we exploit the link informa-

tion from the perspective of each speciﬁc user.

Some studies were made in this line of user-based

personalization. However, approaches do not em-

brace graph-based systems. On Singh et al. (Singh

et al., 2018), user-based collaborative ﬁltering is used

to generate recommendations by utilizing both items

and user preferences based on splitting criteria for

movie recommendation applications. Every single

item and every single user are split into two virtual

items and two virtual users based on contextual val-

ues. The recommender technique is then applied to

the new dataset of split items and users. A new ap-

proach is proposed by Yi et al. (Yi et al., 2018) by

combining the tasks of rating prediction from a rating-

based system with a review-based RS. In this manner,

they made a user-item rating relation from latent fea-

ture representations and fuse it to the user-item review

relation extracted from users’ reviews.

Latest research addresses new approaches to im-

prove the user experience in RS, as in Blanco. et al.

(Blanco. et al., 2021). They propose a recommender

recovery solution with an adaptive ﬁlter to deal with

the failed recommendations, which after a recommen-

dation failure, ﬁlters out all items that are similar to

the one disliked by the user. The objective is to keep

the user engagement and allow the recommender sys-

tem to become a long-term application. Same as Yi

et al., this approach is not adapted to LD-based sys-

tems. Another interesting approach is the use of long-

tail algorithms to generate recommendations that can

lead users to explore less popular but highly relevant

items in the RDF graph (de Sousa Silva et al., 2020).

Our approach aims to improve the user experience as

well, with the advantage of being LD-driven. Gan

et al. (Gan et al., 2021) proposed an EM-model that

alternates between a general item diversity learning

and knowledge graph embedding learning for user

and item representation, which helps to achieve better

Exploiting Linked Data-based Personalization Strategies for Recommender Systems

229

results in comparison to the state-of-art baselines on

datasets MovieLens and Anime. Although this work

takes advantage of auxiliary information along with

historical interactions between user and item from

knowledge graphs, it does not cover a personalization

method.

Cao et al. (Cao et al., 2019) proposed a research

based on the idea that a KG has commonly miss-

ing facts, relations, and entities. Thus, they argue

that it is crucial to consider the incomplete nature of

KG when incorporating it into recommender system.

The main difference between the related work cited

above and our approach fundamentally is that neither

of them really leverages the knowledge present in the

user model (KG) to make personalized recommen-

dations. Among the related works, the one that ap-

proaches most closely the idea of this work is Cao et

al., because they explore the KG trying to understand

the reasons why a user likes an item. They provide

an example that if a user has watched several movies

directed by (relation) the same person (entity), it is

possible to infer that the director relation plays a crit-

ical role when the user makes the decision, thus help

to understand the user’s preference at a ﬁner granu-

larity (Cao et al., 2019). We also started our research

from this idea and developed a weighting algorithm

that ranks properties by importance from the user’s

point of view. Before the ranking step, we also ad-

dress the concept of KG completion, although we use

the rationale of direct and indirect links coming from

Passant (Passant, 2010). We generate the missing di-

rect links by counting the indirect links between the

items the user interacted with, as we demonstrate be-

low.

5 PROPOSAL

This section is dedicated to explaining the strategies

proposed in this work, beginning with the user graph

personalization, and going through how the weights

are applied together with a similarity measure for rec-

ommending items to the user.

5.1 Notation

A user model or user proﬁle is how a recommender

system represents the users’ preferences about items,

as discussed in Section 2. In this work, we model

the user proﬁle as part of the Linked Data graph that

describes the system’s domain. Therefore, we as-

sume a set of RDF resources identiﬁed by their URI

R = {r

,...,r

}, and a set of users identiﬁed by

their URI U = {u

,...,u

}, in which each instance

of u

is also itself a resource in the LD graph, in other

words, U ⊂ R. Figure 2 shows an example of a user

resource u

= dbr:User1, which is represented in yel-

low, to distinguish it from other resources.

We also assume a set of properties P =

, p

,..., p

} that link together pairs of resources

from R molding triples in the form of T =

,...,t

}. Thus, as an RDF triple is a state-

ment in the format hsubject, predicate, objecti, then

= hr

, p

i ∈ T means that there is an instance

of a property p

∈ P linking the subject r

∈ R to

the object r

∈ R, like in hdbr:Toy Story, dbo:director,

dbr:John Lasseteri, back to Figure 2.

Following the DBpedia notation, we elaborated

one property p

= dbr : liked that represents all the

positive feedback from users over the items, with

∈ P. For example, a triple t

= hu

,dbr : liked, r

means that the user u

∈ U has rated the item r

∈ R

as a positive feedback.

In essence, a LOD graph is a directed graph

G = (R,P,T ), that encompass the sets of resources

R = {r

,...,r

}, properties P = {p

, p

,..., p

} and

triples T = {t

,...,t

}, in combination with a set

of special resources called users U = {u

,...,u

and one special property p

= dbr : liked, which to-

gether provide the notation for modeling the user’s

preferences over the dataset items.

5.2 Graph Personalization

Our proposed strategy is composed of a user model

personalization approach (da Silva et al., 2019),

and of an ontology-based feature selection approach

adapted for LD contexts from Di Noia et al. (Di Noia

et al., 2018). In the personalization step, any resource

from R that is linked to a user u

by the particular

property p

is considered as part of the user model.

These resources are always of the same type, which

is the main class of the domain dataset. For exam-

ple, Figure 2 models a portion of a movie dataset,

where we can notice the resources from the movie

class highlighted in lavender color. The three of them

are in the model because the user u

= dbr:User1 has

liked them through the property p

= dbr : liked.

The personalization method intends to assign

weights to every property p

from P in the graph G

that links a user model item — colored in lavender in

Figure 2 and represented in equations by r

— to any

other resource — mostly the ones colored in green,

represented by r

. The goal is to rank properties by

applying weights so that the aspects of the ﬁlm that

inﬂuenced the user the most are at the top of the list. It

means that we assign higher weights to the properties

that occur more among the liked movies. Equation

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

230

1 shows how the weights calculation is made for each

user u

W (p

) =

1 +

∑

Freq(r

)

(1)

Where t

= hr

, p

i ∈ T , and Freq(r

) calculates

the frequency of the resource r

, by iterating over the

triples t

= hr

, p

i ∈ T with i varying from 1 to q.

Since the resource r

always refers to an item that

was positively evaluated by a user u

, the W equation

is performed for every p

until a rank of properties is

generated from the perspective of a particular user u

In future recommendations, the ranking will be taken

into account when calculating the similarity of new

items. In resume, the graph personalization process

is based on the concept that the more one property is

associated with appreciated items, the greater the im-

portance of this property in future recommendations

for the user.

5.3 Personalized LDSD

In this work, we show that ranking the most rele-

vant features from the user’s point of view, instead

of considering just the context model, increases the

effectiveness of the RS. The classic LDSD (Passant,

2010) measure considers every link p

as having the

same weight on calculations. In order to make the

recommender system personalized, we add the func-

tion W (p

) to the PLDSD Equation. As W repre-

sents the weight to one property for a given user’s

point of view, its value is multiplied with each func-

tion C(p

), which are iterated to the j number

of properties linking r

and r

, as shown in Equation

The PLDSD distance counts direct and indirect

links from resource r

to resource r

and vice versa.

All functions C(p

) compute whether there is a

link p

between resources r

and r

. They return 1

in case there is, 0 otherwise. Whenever there is an n,

the function is calculating the total number of links

between a resource r

or r

to all other resources. C

only takes into consideration direct links from r

to r

;

reverse links, from r

to r

, count as different links.

The function C

represents indirect incoming links

and returns 1 only if there is a resource r

that satisﬁes

both hp

i and hp

i, 0 if not. In the case of

returning 1, a virtual direct link is created between the

two resources that share the incoming link. The func-

tion C

represents indirect outgoing links and equals

to 1 only if there is a resource r

that satisﬁes both

i and hp

i, 0 if not. In the case of re-

turning 1, a virtual direct link is created between the

two resources that share the outgoing link.

In Figure 2 the highlighted property dct:subject

was created because The Amazing Spider Man and

The Avengers share the same outgoing link. Simi-

larly, the property dbo:product was created because

The Avengers and Toy Story share the same ingoing

link. It is possible to infer from the ﬁgure that these

two properties will have a greater weight after the W

equation is calculated, because they appear more fre-

quently in the graph, inﬂuencing the PLDSD result.

5.4 Semantic Feature Selection

We adapted the ABSTAT Feature Selection method

(Di Noia et al., 2018) by applying the LDSD mea-

sure to it and implemented a preprocessing step that

ﬁlters out the most important features for each do-

main. That method originally uses the Jaccard index,

a well-known but non-speciﬁc similarity measure for

LD-based systems.

ABSTAT is the name of the original implementa-

tion (Di Noia et al., 2018), which consists of an au-

tomatic FS method, the ontology-based data summa-

rization framework. This framework identiﬁes pat-

terns and frequencies and computes cardinality de-

scriptors from the RDF triples of one domain. The au-

thors conduct experiments in three different domains:

movies, music and books.

6 EXPERIMENTAL EVALUATION

The goal of the experimental evaluation is to validate

the hypothesis that personalized user models can lead

to more effective recommendations in Linked Data-

based systems. To prove this, we implement a person-

alization strategy that weighs and ranks each property

in the user model according to the user’s past interac-

tions with the system, called PLDSD.

We performed two comparative experiments

against the classical LDSD method, one using a

movie RS and one using a music RS. Results show

greater accuracy of the system when using PLDSD

instead of the non personalized LDSD. Additionally,

we run the PLDSD method with and without the pre-

processing step — the semantic Feature Selection

method — as additional validation experiments. We

demonstrate that using a ﬁlter technique appropriate

to the application domain — both by being LD-based

sim

PLDSD

) =

1 +

∑

).W (p

)

1+logC

,n)

∑

).W (p

)

1+logC

,n)

∑

).W (p

)

1+logC

,n)

∑

).W (p

)

1+logC

,n)

(2)

Exploiting Linked Data-based Personalization Strategies for Recommender Systems

231

Figure 2: Example of weight calculation in PLDSD.

and by taking into account the semantics of the do-

main elements — also increases the accuracy of the

recommendations when combined with the personal-

ization strategy. We critically discuss all these results

in Section 7.

The experiments were implemented in Java using

JENA, an open source framework for building Se-

mantic Web and Linked Data applications

. We also

store similarity values and other data in a MySQL

database and leverage the linked data environment

with the Virtuoso Server

. The complete implemen-

tation project has been made available on an Anony-

mous Github under the url https://anonymous.4open.

science/r/lodweb-pldsd-40BD/. Researchers inter-

ested in replicating and conducting further compar-

ative studies can ﬁnd the necessary documentation at

this location.

6.1 Dataset Setup

Two datasets were conﬁgured to run the experimen-

tal evaluations. The ﬁrst dataset is the Movielens 1M

dataset

, which has: 1 million ratings from 6000 users

on 4000 movies (1-5 stars rating). The Dataset rating

density is 4.26%. From this start point, we modeled a

database that stores the user model — users, movies,

and user ratings over movies, and also stores the con-

tent of movies in the form of RDF triples. For this

purpose, we used the MappingMovielens2DBpedia

project

, which provides RDF identiﬁers — URIs —

https://jena.apache.org/

https://virtuoso.openlinksw.com/

https://grouplens.org/datasets/movielens/1m/

https://github.com/sisinﬂab/LODrecsys-datasets/blob/

master/Movielens1M/MappingMovielens2DBpedia-1.2.tsv

for each movie in MovieLens 1M. Therefore, this ini-

tial setup allows us to access resources and all their

connections from DBpedia through SPARQL queries

online.

The second dataset is the Last.FM Million Song

It contains listening information from almost 2 thou-

sand users about approximately 1 million songs.

Nonetheless, we take the listening data summarized

to user-artist for this work, rather than considering

the songs played. For this reason, during the dataset

setup step, we used another set of data released dur-

ing the HETRec 2011 Workshop and published as an-

other project of raw data mapping to Linked Data

As is in the movie mapping project, this one provides

DBpedia URIs for each musical artist or band in the

Last.FM dataset.

6.2 Methodology

In this work, we simulate two recommender sys-

tems that retrieve the Top-N recommendations to the

user: a Movie RS and a Music RS. User prefer-

ences about movies in the MovieLens dataset are ex-

pressed on a 5-star Likert scale. Some work on the

MovieLens dataset use methods for making the user

ratings binary (Di Noia et al., 2012; Musto et al.,

2016). The PLDSD methodology considers 3 differ-

ent scenarios to represent a user’s positive feedback:

PLDSD

rat=5

, that only ratings equal to 5 are consid-

ered; PLDSD

rat>=4

that considers movies rated as 5

and 4; and PLDSD

rat>=3

that takes ratings equal to 5,

http://millionsongdataset.com/lastfm/

https://github.com/sisinﬂab/LinkedDatasets/blob/

master/last fm/mappingLinkedData.tsv

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

232

4 and 3 as positive feedback. Previous work (da Silva

et al., 2019) demonstrated that the PLDSD

rat=5

strat-

egy performed the most accurate results for all sce-

narios. For this reason, we adapted our methodology

to consider ratings 5 as positive feedback and all the

other ratings — 1, 2, 3, and 4 — as negative feedback.

Similarly, in the Last.FM dataset, user preferences

about artists are expressed through the number of

times that each user has listened to their songs. Con-

sequently, the concept of positive feedback for this

work had to be constructed by isolating an expres-

sive number that represents a user listening to a well-

liked artist throughout their daily routine. We chose

the number 500 to represent positive feedback and

therefore artists tracks heard less than 500 times are

considered negative feedback given by a user. Based

on this, we have built the user models from the set

of items positively evaluated by each user. We also

personalized the user models by applying the PLDSD

methodology as explained in section 5.

The personalization step results in lists of ranked

properties according to the user’s past choices in the

system. These lists may contain all the properties in-

volved in the user model, or they may be limited by a

cutoff point, which is the value of the parameter k in

the ontology summarization approach (section 3). In

other words, the parameter k in the feature selection

preprocessing step deﬁnes how many properties will

be ranked by the personalization step.

From this point on, we will refer to movies and

musical artists simply as items, since the methodol-

ogy applies to any domain and we would like to de-

scribe it as generally as possible. First, we take items

that the user has given positive feedback and build the

training dataset or the dataset of known data. Then we

take a subset of 90 items randomly (discarding those

that are already in the user model subset) and build

the test dataset. After that, we perform the PLDSD

algorithm on the test dataset and also perform the

LDSD algorithm as the baseline method. The user

model is then used twice during each round of exper-

iments: ﬁrst to perform the weigh preprocessing step

described in Subsection 5; and secondly as the vali-

dation dataset to which we compare the ranked result

list coming from the recommendation method.

Moreover, we performed some statistical tests to

assess the signiﬁcance of the experimental results.

First, we ran the chi-squared goodness-of-ﬁt test over

the ranked lists of movies and music. As this test re-

vealed the normal distribution of the data, we opted

to apply a paired T-test using a p-value << 0.0001 to

each PLDSD sample — with 3 variations of k values

— against its correspondent baseline method (LDSD)

results’ sample — with 3 more variations of k values.

As the length of the sample varies according to how

many items the user has rated, we ran statistical tests

for each user tested.

6.3 Metrics

We compute the results under 3 evaluation metrics to

assess the quality of the generated ranks. These met-

rics assess the relevance and accuracy of the similarity

algorithm. In other words, they measure how accu-

rately a system can predict similar items to a speciﬁc

user.

• Precision@K - Precision measures the fraction of

retrieved items from a dataset that are relevant, as

shown in Equation 3. Precision@K is a variation

that represents the amount of relevant results at

the position K (Equation 4). We have adopted in

our evaluation two classic values of K: 5 and 10

(Davoodi et al., 2013).

Precision =

|RelevantItems ∩ RetrievedItems|

|RetrievedItems|

(3)

Precision@K =

|RelevantItems@K|

(4)

• Mean Average Precision (MAP) - It is a com-

bination of averages that has good discrimination

and stability (Manning et al., 2008). MAP is an

extension of the Average Precision (AP) which

takes the average of all AP as shown in Equation

MAP(Q) =

|Q|

∑

j=1

∑

k=1

Precision(R

) (5)

Given the set {d

,...,d

} of relevant documents

for an information need q

∈ Q and R

k is the set

of ranked results from the top result until docu-

ment d

• Normalized Discounted Cumulative Gain

(NDCG) - It is a metric based on the notion that

items in a rank have varying degrees of relevance,

either once relevant items appear in a low-rank

position or when less relevant items appear in a

greater position. (J

arvelin and Kek

ainen, 2002).

Thereby, the gained value (without discounts) is

obtained as the relevance score of each item is

progressively summed from the rank position 1

to n. Discounted Cumulative Gain (DCG) is then

obtained through Equation 6, which penalizes

highly relevant documents appearing lower on a

search result list as the graded relevance value

is logarithmically reduced proportionally to the

result’s position.

Exploiting Linked Data-based Personalization Strategies for Recommender Systems

233

DCG[i] =

(

CG[i], if i < b

DCG[i − 1] + G[i]/log

i, if i ≥ b.

(6)

DCG values can be compared to the theoretically

best possible score vector, the ideal vector, which is

represented by Equation 7 for relevance scores 0 and

1, where m is the number of relevant items.

BV [i] =

(

1, if i ≤ m

0, otherwise.

(7)

Normalizing the values is a required step when

comparing two techniques under DCG. This is made

by dividing the DCG vectors by the corresponding

ideal DCG vectors (iDCG). The NDCG is then calcu-

lated using Equation 8 which, likewise Precision@K,

only evaluates the top k results (Manning et al., 2008).

NDCG(k) =

DCG(k)

iDCG(k)

(8)

7 RESULTS

This section presents and discusses the results of each

evaluation metric, considering the methodology and

objectives proposed by this work. Before the tests, we

built the user models from the set of items positively

evaluated by each user. The concept of positive rating

was constructed differently for each domain dataset,

movies, and music, according to the methodology ex-

plained in section 6.

The personalization step results in lists of ranked

properties, that are used to feed the recommender sys-

tem, which runs two similarity algorithms: LDSD and

Personalized LDSD (PLDSD). The ranked lists may

be limited by a k value due to the feature selection pre-

processing step. As we consider 3 k values for each

strategy — in addition to the turn without limiting the

value of k —, and 2 similarity strategies, the experi-

mental evaluation consists of 8 rounds of testing for

each user on the movie dataset and another 8 rounds

for each user on the music dataset.

7.1 Discussion

In order to facilitate the understanding of the results

tables, we use the acronym LDSD to represent the

recommender model built under plain LDSD, and

the acronym PLDSD to represent the recommender

model that considers our personalized approach, as

presented in Section 5. Tests that consider the feature

selection step are identiﬁed by the value assigned to k

in the result tables: Table 1 and Table 2. For exam-

ple, PLDSD k=10 means that results encompass the

personalization method and the preprocessing step,

that selects the 10 most relevant properties. On the

other hand, LDSD k=0 demonstrates the results for

when neither customization nor feature summary are

applied.

Table 1 summarizes the average (Avg) results of

a hundred users from the movie dataset, considering

the LDSD scenarios with and without feature selec-

tion and PLDSD with and without feature selection.

In the movie scenario, each of the 100 user models is

composed of at least 7 and at most 52 positively rated

movies. The test set is composed of 200 not evaluated

movies. The value of k varies from 10 to 535, which is

the maximum number of properties for the movie do-

main in DBpedia. LDSD and PLDSD without feature

selection (k=0) are used for comparison purposes.

Results of Table 1 show a statistically signiﬁcant

increase on all metrics when applying the PLDSD

with k=10. We can notice that the LDSD approach

presents worse results in comparison with the person-

alized and ﬁltered rounds.

Table 1: Results for the movie dataset considering diverse

scenarios.

Strategy Precision@5 Precision@10 MAP NDCG

LDSD k=0 0.450 0.450 0.501 0.743

LDSD k=10 0.500 0.450 0.565 0.798

LDSD k=100 0.450 0.425 0.550 0.751

LDSD k=535 0.450 0.425 0.499 0.744

PLDSD k=0 0.450 0.450 0.511 0.757

PLDSD k=10 0.550 0.450 0.582 0.809

PLDSD k=100 0.550 0.450 0.581 0.794

PLDSD k=535 0.550 0.425 0.504 0.783

Table 2 summarizes the average (Avg) results of

a hundred users from the music dataset, considering

LDSD scenarios with and without feature selection

and PLDSD with and without feature selection. In the

music scenario, each of the 100 user models consists

of at least 14 and at most 50 positively rated artists.

The test set is composed of other 200 not evaluated

artists. The value of k varies from 10 to 512, which is

the maximum number of properties for the music do-

main in DBpedia. LDSD and PLDSD without feature

selection (k=0) are used for comparison purposes.

As with the movie domain, the music results

shown in Table 2 also achieved higher values when

applying PLDSD with k=10. And again the results

with LDSD without personalization and without ﬁl-

ters show worse results among the rounds of experi-

ments.

The results for the movie dataset experiments

show that MAP and NDCG metrics obtained the best

values. Additionally, those values are higher when the

similarities are calculated using the feature selection

step. Figure 3 presents the MAP and NDCG values

for both LDSD and PLDSD approach with different

WEBIST 2022 - 18th International Conference on Web Information Systems and Technologies

234

Table 2: Results for the music dataset considering diverse

scenarios.

Strategy Precision@5 Precision@10 MAP NDCG

LDSD k=0 0.433 0.450 0.491 0.622

LDSD k=10 0.462 0.450 0.520 0.671

LDSD k=100 0.425 0.425 0.503 0.621

LDSD k=512 0.420 0.425 0.488 0.603

PLDSD k=0 0.436 0.450 0.509 0.635

PLDSD k=10 0.470 0.556 0.535 0.731

PLDSD k=100 0.451 0.445 0.508 0.647

PLDSD k=512 0.450 0.440 0.500 0.620

values of k. This graphical representation emphasizes

the signiﬁcant growth of approximately 7% in the

NDCG for LDSD values and of 8% in the NDCG for

PLDSD values. Concerning the MAP metric, exper-

iments achieved 13% of growth in the LDSD rounds

and 14% in the NDCG for PLDSD values when ap-

plying the feature selection step.

The results of Precision@5 and Precision@10 in

the movie experiments show positive and negative os-

cillations between values, highlighting a positive gain

of Precision@5 when calculating PLDSD with the

feature selection. Furthermore, all metrics results are

higher when the number of properties is reduced in

the pre-selection step, both for LDSD and PLDSD,

especially when k = 10.

Figure 3: NDCG and MAP results versus the k value of

pre-selected features from the movie domain.

Figure 4: NDCG and MAP results versus the k value of

pre-selected features from the music domain.

The results of the experiments with the music

dataset also showed improvements, although not as

expressive as the movie domain, especially for the

MAP metric. Figure 4 shows a growth of 8% in the

NDCG for LDSD values and of 15% in the NDCG for

PLDSD values. Regarding the MAP metric, exper-

iments achieved 6% of growth in the LDSD rounds

and 5% in the NDCG for PLDSD values when apply-

ing the feature selection step.

Originally, the ABSTAT method behave differ-

ent depending on the selected knowledge domain

(Di Noia et al., 2018). A comparative analysis leads

us to conclude that ABSTAT summaries are strongly

grounded in the ontological nature of the knowledge

graph, while our approach emphasizes user prefer-

ences drawn from their previous interactions with the

system. This means that PLDSD applied to different

domains will perform similarly, although it may vary

slightly. Nevertheless, the combined use of the two

techniques has been shown to be more efﬁcient than

either one separately.

However, further analysis is required to investi-

gate if the nature of the music subject does not allow

an accurate prediction of its properties weigths. The

maximun number of properties retrieved from DB-

pedia is similar to both domains — 535 and 512 —

although the modeling is very different. A musical

artist can be linked to many different songs by the

property is dbo:artist of, while an actor is apt to be

linked to fewer movies by the property is dbo:star of.

For example, the band The Rolling Stones is linked

to over 300 songs, while Anthony Hopkins, the actor

with one of the most solid careers, could act in only

137 movies so far.

Conversely, the overall performance of the system

shows a signiﬁcant reduction in the processing time,

both for the movie and song datasets, since the num-

ber of properties being computed is reduced by ap-

proximately 80% when considering K = 10. Taking

the example of user #1 — who positively rated 246

artists — the computation time was reduced from 240

to 67 minutes by adding the selection step, a reduction

of 72%.

7.2 Points of Improvement

During the evaluation phase, one difﬁculty was to de-

ﬁne the number of properties that should be used in

the pre-selection process, that is, to deﬁne the value of

k. Due to this fact, the tests were performed consider-

ing three distinct values for k. We deﬁned the values

so that the algorithm could explore a low (k = 10),

a medium (k = 100) and a high (k = max) value of

k for pre-selection. The highest value of k is the

maximum number of properties returned by the pre-

selection step with the dataset used in the tests, which

is k = 535 for movies and k = 512 for music.

Although most of the results were more relevant

when k = 10, both for LDSD and PLDSD, it is neces-

sary to establish a method that identiﬁes the optimal

amount of properties to be analyzed for each data set.

Exploiting Linked Data-based Personalization Strategies for Recommender Systems

235

One suggestion for future work is to apply the Elbow

Curve, a popular method for ﬁnding the optimal num-

ber of clusters when working with the K-Means clas-

siﬁcation algorithm (Kaufman and Rousseeuw, 1990).

We propose to investigate the possibility of ﬁnding the

ideal value to K by adapting the Elbow Method, with

the purpose of improving the results personalization

strategies of this work.

8 CONCLUSION

In this paper, we proposed personalization strategies

for LD-based recommender systems. We use a user

modeling process that analyses the past interactions

of the user with the system to rank the properties that

are used in the recommender model. After ranking

the properties, we applied the Personalized Linked

Data Semantic Distance (PLDSD) similarity measure,

which generates a rank of items to recommend to the

user. We run experiments comparing the PLDSD re-

sults to the classic LDSD measure using 3 different

metrics. We also performed comparative experiments

using an adapted implementation of an LD-oriented

feature selection strategy, so that only the most rele-

vant properties for the system were considered in part

of the calculations.

The evaluation results show the best values for

PLDSD combined with a k = 10 choice of feature

selection strategy, that outperforms the unweighted

and not ﬁltered baseline method LDSD. We can state

that this work achieved the goal of obtaining better

accuracy and performance of LD-based RS when us-

ing movie and music datasets from DBpedia. An im-

provement in the results was noticed when the num-

ber of items evaluated was increased and the number

of selected properties was reduced with the applica-

tion of the ﬁltering step. The results of the PLDSD

metrics combined with the ﬁlter properties stood out

from the others in all the tests performed.

As future work, we aim to test our model us-

ing other LD-based similarity measures in order to

compare and determine which one performs better.

We also plan to conduct more studies regarding the

feature selection task, by using other LD-driven ap-

proaches and comparing them to the baseline meth-

ods used so far. Another possible future work is to

evaluate this approach using a cross-domain dataset,

which would enable the development of multi-domain

recommendations for general use in linked datasets.

ACKNOWLEDGEMENTS

This work was partially funded by the Coordenac¸

de Aperfeic¸oamento de Pessoal de N

ıvel Superior -

Brasil (CAPES) – Grant number 001.

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