CONTENT-BOOSTED COLLABORATIVE FILTERING USING
SEMANTIC SIMILARITY MEASURE
Ugur Ceylan
Department of Computer Engineering, Baskent University, Ankara, Turkey
Aysenur Birturk
Department of Computer Engineering, METU, Ankara, Turkey
Keywords:
Content-boosted collaborative filtering, Semantic similarity, Ontology, Sparsity, Item cold-start.
Abstract:
Collaborative filtering is one of the most used recommendation approaches in recommender systems. How-
ever, collaborative filtering systems have some major problems such as sparsity, scalability and cold-start
problems. In this paper we focus on the sparsity and item cold-start problems in collaborative filtering in order
to improve the quality of recommendations. We propose an approach that uses semantic similarities between
items based on a priori defined ontology-based metadata in the movie domain. According to the semantic sim-
ilarities between items and past user preferences, recommendations are made. The results of the evaluation
phase show that our approach improves the quality of recommendations.
1 INTRODUCTION AND
RELATED WORK
The aim of recommender systems is to predict the
valuable information/items for a user and recommend
these items. Some examples of items that are rec-
ommended by recommender systems are web pages,
movies, music, books, restaurants, etc.
One of the most commonly used recommendation
approaches in recommender systems is collaborative
filtering. The idea behind collaborativefiltering is that
similar users almost have the same opinion about an
item. Collaborative filtering systems try to find the
similarities between active user and other users in the
system, and then recommend items to the active user
by taking into account these similarities. But, col-
laborative filtering systems suffer from some prob-
lems such as sparsity and item cold-start problems
(Melville et al., 2002; Claypool et al., 1999).
Content-based filtering is another recommenda-
tion approach that is used widely. In content-based
filtering, the system tries to recommend items which
have similar contents with the items that are liked
by the users. But also, content-based filtering sys-
tems have some major problems (Balabanov´ıc and
Shoham, 1997). For some domains in which extract-
ing content of items is difficult and content of items
are insufficient to express items, content-based filter-
ing is not a suitable recommendation approach. An-
other problem in content-based approach is that it
tends to recommend items that are similar to those
already highly rated. This problem is called over-
specialization problem.
Some techniques are used in order to cope with
sparsity and item cold-start problems. The simplest
technique, which is used to overcome the sparsity
problem, is called default voting (Breese et al., 1998).
In this technique, a default rating is inserted for items
which don’t have rating values given by either one of
them or the other. Thus, the number of overlapping
rated items by both users is increased. The other tech-
nique for dealing with the sparsity problem is using
dimensionality reduction techniques such as Singu-
lar Value Decomposition (SVD) (Sarwar et al., 2000).
By applying SVD, user-item rating matrix may be-
come less sparse.
Hybrid recommendation approach is gener-
ally implemented by combining collaborative and
content-based filtering approaches to cope with the
drawbacks of these two filtering methods (Bala-
banov´ıcand Shoham, 1997). Some hybridapproaches
are as follows. One approach to use both content-
based and collaborative filtering approaches is com-
bining them. In this approach, system generates rec-
ommendations by using content-based and collabo-
rative filtering approaches and then combines these
366
Ceylan U. and Birturk A..
CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE.
DOI: 10.5220/0003402403660371
In Proceedings of the 7th International Conference on Web Information Systems and Technologies (WEBIST-2011), pages 366-371
ISBN: 978-989-8425-51-5
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

independent recommendations (Pazzani, 1999). An-
other approach gives some weight values to collab-
orative and content-based predictions. Then hybrid
approach takes the weighted sum of the predicted rat-
ings. And finally items that are recommended are se-
lected based on the calculated weighted sum (Clay-
pool et al., 1999). Also content-based filtering is used
to complete the missing data in the user-item rating
matrix. Then collaborative filtering approach is used
to recommend items to users. This hybrid approach
is also called content-boosted collaborative filtering
(Melville et al., 2002).
In this paper, we propose a hybrid approach based
on content-boosted collaborative filtering presented
in (Melville et al., 2002). The purpose of our ap-
proach is to cope with sparsity and item cold-start
problems of collaborative filtering. Our approach uti-
lizes both content-based and collaborative filtering.
The crucial point in this study is that the content-
based filtering in our approach uses semantic sim-
ilarity measures on ontology-based metadata, based
on the studies in (Maedche and Zacharias, 2002) and
(Lula and Paliwoda-Pekosz, 2008), instead of naive
Bayesian classifier (Mitchell, 1997) which is men-
tioned in (Melville et al., 2002). After that, col-
laborative filtering is performed using enhanced data
to overcome over-specialization problem in content-
based filtering.
2 PROPOSED APPROACH
The flowdiagram of our approach, called SEMCBCF,
is shown in the Figure 1. SEMCBCF consists of three
phases:
1. Generating ontology-based metadata
2. Finding enhanced user-item rating matrix by us-
ing content-based filtering
3. Using collaborative filtering on enhanced user-
item matrix
2.1 Generating Ontology-based
Metadata
In order to find semantic similarities between items,
ontology model and metadata model have to be de-
fined. Ontology and metadata models are defined as
follows (Maedche and Zacharias, 2002):
O := {
˜
C,
˜
P,
˜
A,H
c
, prop,att} (1)
MD := {O,
˜
I,
˜
L,inst,instl,instr} (2)
For ontology model,
˜
C,
˜
P and
˜
A are sets which con-
sist of concepts, relations and attributes’ identifiers
User-Item
Rating
Matrix
Content-based
Filtering using
Semantic Similarity
Ontology-based
Metadata
Enhanced
User-Item
Rating
Matrix
Collaborative
Filtering
Ontology-based
Metadata Creation
Movie
Ontology
IMDb
Active User
Ratings
Vector
Recommendations
Figure 1: System Overview.
respectively. H
c
is called concept taxonomy
which defines the hierarchical relations between con-
cepts. prop and att are functions that define non-
taxonomical relations. For metadata model,
˜
I and
˜
L
are sets which consist of instances and literal values
respectively. inst, instr, instl are functions that de-
fine concept instantiation, relation instantiation and
attribute instantiation. Predicates and their meanings
that are used in the following sections are shown in
the Table 1.
Table 1: Predicates and Meanings.
Predicate Meaning
H
c
(C
1
,C
2
) C
1
is a subconcept of C
2
P(C
1
,C
2
) P is a relation with
domain C
1
and range C
2
A(C
1
) A is an attribute of C
1
C(I) I is a instance of concept C
P(I
1
,I
2
) Instance I
1
has a P relation
to instance I
2
A(I
1
,L) Instance I
1
has an A attribute
with value of L
To define movie ontology, we use a free, open
source ontology editor and knowledge-base frame-
work called Protege (http://protege.stanford.edu). By
using Protege, we create a number of different movie
ontologies manually. Then, the metadata are gener-
ated based on the defined movie ontology and the
content of movies extracted from IMDb (The Inter-
net Movie Database, htt p : //www.imdb.com). Con-
CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE
367

tent of a movie is represented as 10 dimensions which
consist of cast, director, writer, language, genre, run-
time, release date, country, color and average rating
given by IMDb users. A feature of a dimension can
be an instance or a concept or a literal in ontology.
In order to evaluate our approach, four different
ontologies are used. Ontology1 is the basic ontology
used in our approach. All features of genre dimension
are sub-concept of Movie concept. For each remain-
ing dimensions, a concept exists in Ontology1. And
the features of a dimension are instances of its corre-
sponding concept. Ontology2 is similar to Ontology1.
Only difference between them is that concepts repre-
sent dimensions except genre has more sub-concepts
in Ontology2 than it has in Ontology1. For example
the concept that represents runtime dimension has a
number of sub-conceptsthat defines the runtime inter-
vals. The only difference between Ontology1 and On-
tology3 is that runtime, release date and average rat-
ing dimensions are represented as attributes in Ontol-
ogy3. In Ontology4, the features of genre dimensions
are grouped into six sets and a set represents a con-
cept. A feature of genre dimension is a sub-concept
of its corresponding concept.
2.2 SEMCBF:Content-based Filtering
using Semantic Similarity
In order to recommend items, a similarity measure be-
tween items has to be defined first. And then, by us-
ing the active user model, which is a vector that con-
sists of user’s ratings, and similarities between items,
we predict the ratings of unrated items which will
be given by the active user. In SEMCBF, to calcu-
late similarities between items which are described by
ontology-based metadata, we use three types of sim-
ilarity measures(Maedche and Zacharias, 2002): tax-
onomy similarity (TS), relation similarity (RS), and
attribute similarity (AS).
Taxonomy similarity between two instances (TS)
is based on their corresponding concepts’ positions in
concept taxonomy (H
c
) which is defined in ontology
model. Basically, the idea behind taxonomy similar-
ity is that closer concepts in taxonomy are more sim-
ilar.
An instance can be instance-of two different con-
cepts in ontology. So, to find taxonomy similarities
between instances (TS), first, taxonomy similarities
between concepts (TSC) have to be defined.
In order to calculate TSC, four different meth-
ods, TSC
CM
, TSC
Wu&Palmer
, TSC
Lin
and TSC
Mclean
in
the studies (Maedche and Zacharias, 2002), (Wu and
Palmer, 1994), (Lin, 1998), (Li et al., 2003) respec-
tively, are used.
After finding the taxonomy similarity between
concepts, calculating taxonomy similarity between
instances is reduced to calculating the similarity of
two sets. So TS is defined as follows:
TS(I
1
,I
2
) = SSIM(CSET(I
1
),CSET(I
2
)) (3)
where CSET(I) = {C ∈
˜
C|C(I)}.
Similarity between two sets can be found using
the similarities between their elements, in this case
TSC of concepts, and using different methods. These
methods are mentioned later in this section.
The second type of similarity measure using
ontology-based metadata is relation similarity. Rela-
tion similarity (RS) between two instances is based
on their relations to other instances in ontology-based
metadata. For relation similarity measure, we use
a modified version of relation similarity measure in
(Maedche and Zacharias, 2002). RS between I
1
and
I
2
can be calculated as follows:
RS(I
1
,I
2
) =
∑
p∈P
co−I
OR(I
1
,I
2
, p, IN)
|P
co−I
| + |P
co−O
|
+
∑
p∈P
co−O
OR(I
1
,I
2
, p, OUT)
|P
co−I
| + |P
co−O
|
(4)
P
co−I
stands for ’incoming relations’ and is
the set of relations that allows UC(C(I
1
),H
c
) and
UC(C(I
2
),H
c
) as range where UC(C
i
,H
c
) = {C
j
∈
˜
C|H
c
(C
i
,C
j
) ∨ C
i
= C
j
}. P
co−O
stands for ’outgo-
ing relations’ and is the set of relations that allows
UC(C(I
1
),H
c
) and UC(C(I
2
),H
c
) as domain.
OR(I
1
,I
2
, p, DIR) stands for the similarity for re-
lation p and direction DIR between instances I
1
and
I
2
where DIR ∈ {IN,OUT}. OR(I
1
,I
2
, p, DIR) can
be calculated by considering associated instances of
I
1
and I
2
with respect to relation P and direction DIR.
Associated instances (A
s
) of instance I
i
with respect
to relation P and direction DIR is as follows:
A
s
(P, I
i
,DIR) =

{I
j
: I
j
∈
˜
I ∧ (P(I
j
,I
i
)}, if DIR = IN
{I
j
: I
j
∈
˜
I ∧ (P(I
i
,I
j
)}, if DIR = OUT
(5)
After defining A
s
, calculating OR(I
1
,I
2
, p, DIR) is re-
duced to calculating the similarity of two sets that
contains associated instances. So OR is defined as
follows:
OR(I
1
,I
2
, p,DIR) = SSIM(A
S
(P, I
1
,DIR),A
S
(P, I
2
,DIR))
(6)
If A
S
(P,I
1
,DIR) =
/
0 or A
S
(P,I
2
,DIR)) =
/
0 then
OR(I
1
,I
2
, p, DIR) is 0. The point is that to calculate
SSs of instances, RS is used and to calculate RSs of in-
stances, SSs of associated instances are used. In order
to avoid infinite cycles, a maximum depth of recur-
sion has to be defined.
WEBIST 2011 - 7th International Conference on Web Information Systems and Technologies
368

The advantage of using relation similarity is that
the similarities of features are taken into account.
Suppose that, in a system, movies have only one fea-
ture which is an actor played in movie and we try to
find the similarity between two movies, MovieX and
MovieY. MovieX has a feature ActorA and MovieY
has a feature ActorB. If a user only rated movies in
which only ActorA played, it is unable to predict the
rating of MovieY by using naive Bayesian classifier.
But in SEMCBF, similarity of MovieX and MovieY
depends of the similarity between ActorA and Ac-
torB. In a recursive manner, the similarity of ActorA
and ActorB depends on similarity of other instances
which have relations with ActorA and ActorB. Thus,
we can calculate a similarity value between these two
movies and a prediction can be made.
Attribute similarity is the third similarity mea-
sure that is used to calculate semantic similarities in
ontology-based metadata. AS between instances I
1
and I
2
is as follows:
AS(I
1
,I
2
) =
∑
a∈P
A
OA(I
1
,I
2
,a)
|P
A
|
(7)
where P
A
represents the set of attributes that are at-
tributes of both UC(C(I
1
),H
c
) and UC(C(I
2
),H
c
).
OA(I
1
,I
2
,a) is the similarity for attribute a be-
tween instances I
1
and I
2
. Like calculation of OR,
OA(I
1
,I
2
,a) is calculated by considering associated
literals of I
1
and I
2
with respect to the attribute a. As-
sociated literal (A
l
) of I
i
with respect to the attribute
A is the following:
A
l
(A,I
i
) =

L
x
, if L
x
∈
˜
L∧ A(I
i
,L
x
)
/
0, otherwise
(8)
The difference between A
s
and A
l
is that A
l
can con-
tain at most one literal unlike A
s
. Thus, rather than
calculating similarity of two sets, similarity between
attribute values is focused in order to calculate OA
which is as follows:
OA(I
1
,I
2
,a) = LSIM(L
1
,L
2
,a) (9)
where L
1
= A
l
(a,I
1
) and L
2
= A
l
(a,I
2
). If L
1
=
/
0 or
L
2
=
/
0 then OA(I
1
,I
2
,a) is 0. In our approach, all
of the attributes used are numeric features of the in-
stances like release date (as a year), runtime etc. So,
the similarity between two numeric values (L
1
and L
2
)
of an attribute (a) is as follows:
LSIM(L
1
,L
2
,a) = 1−
(L
1
− L
2
)
MDIF(a)
(10)
where
MDIF(A) = max{(L
i
− L
j
) : A(I
1
,L
i
) ∧
A(I
2
,L
j
) ∧ I
1
,I
2
∈
˜
I} (11)
In order to calculate TS and RS, we have to de-
fine the similarity between sets of elements. Elements
of these sets are, concepts for TS calculation and in-
stances for RS calculation. Similarities of elements
are TSCs for TS and SSs for RS. The first three meth-
ods SSIM
1
, SSIM
2
and SSIM
3
used for calculating the
similarity between sets are the methods in the studies
(Maedche and Zacharias, 2002), (Tintarev and Mas-
thoff, 2006), (Bach and Kuntz, 2005) respectively.
The other methods used for calculating the sim-
ilarity between sets are based on the methods used
for calculating the distance of pair of clusters in
hierarchical clustering algorithms. These methods
are single-link (SSIM
S
), complete-link (SSIM
C
) and
average-link (SSIM
A
) (Maimon, 2005).
Up to now, the taxonomy, relation and attribute
similarities between instances are defined. Now, we
can combine these measures by giving them some
weight values. Semantic similarity (SS) between two
instances is defined as follows:
SS(I
1
,I
2
) =
aTS(I
1
,I
2
) + bRS(I
1
,I
2
) + cAS(I
1
,I
2
)
a+ b+ c
(12)
The last step of SEMCBF is prediction of the un-
known ratings of the items given by users. In order
to predict the unknown ratings, our approach uses
the calculated similarities between items and user
model which consists of ratings given by the user
on a neighborhood-based method (Herlocker et al.,
1999)(Sarwar et al., 2001). To compute a prediction,
two prediction functions can be used after selecting
the k most similar items. First function calculates the
predicted rating of user u on item i by taking the av-
erage of ratings given by the user u on k most similar
items to i. Second function calculates the predicted
rating of user u on item i by taking the weighted aver-
age of the ratings given by the user u on k most similar
items to i. Weights of the ratings are set according to
the similarities between items.
Using user-item rating matrix, SEMCBF creates
enhanced user-item rating matrix. In other words, the
sparsity of user-item rating matrix is reduced. And
also, even if an item has no explicit rating given by
any user in the system, by using SEMCBF, our ap-
proach predicts a rating given by every user for that
item.
2.3 Collaborative Filtering
In the third phase of our approach, a neighborhood-
based (Herlocker et al., 1999) collaborative filtering
algorithm is used on enhanced user-item matrix and
active user ratings vector which consists of only ac-
tual ratings given by the active user. The algorithm
CONTENT-BOOSTED COLLABORATIVE FILTERING USING SEMANTIC SIMILARITY MEASURE
369

first computes the similarity between the active user
and other users in enhanced user-item matrix using
Pearson correlation. After computing similarities be-
tween active user and other users, n number of most
similar users is selected. An unknown rating is pre-
dicted by calculating the adjusted weighted sum of
the nearest neighbors’ ratings of active users.
At the end of a recommendation process, the sys-
tem recommends a number of unrated items which
have the highest predicted rating to the active user.
3 EXPERIMENTAL EVALUATION
The performance of proposed approach was evaluated
in the movie domain by using the MovieLens 100k
dataset (http://www.grouplens.org) which is publicly
available. We apply 5-fold cross-validation on the
disjoint test sets (20% of rating data) and their cor-
responding training sets (80% of rating data) that are
also provided in MovieLens 100k dataset. In ex-
perimental evaluation, we use mean absolute error
(MAE), precision, recall and F-measure performance
metrics which are commonly used to evaluate the per-
formance of recommender systems (Herlocker et al.,
1999). The evaluation of our approach consists of two
phases. In the first phase of the evaluation we try to
find the most appropriate values for SEMCBF param-
eters. In the second phase, the results of SEMCBF
and SEMCBCF is compared with some other ap-
proaches.
SEMCBF consists of some parameters as men-
tioned in section 2. These parameters and their possi-
ble values are shown in Table 2. It is obvious that the
performance of SEMCBF depends on the values of
these parameters. To find the most appropriate value
of a parameter, performance of SEMCBF is evalu-
ated using all possible combinationsof this parameter,
O and k while the values of other parameters remain
constant. In each evaluation, the determined value as
the most appropriate value for a parameter is used in
later evaluations.
At the beginning of the evaluation, parameters rd,
a, b, c, TSC, SSIM
TS
, SSIM
RS
are set to 1, 0.4, 0.3,
0.3, TSC
CM
, SSIM
SSIM
1
, SSIM
SSIM
1
respectively. The
parameters are analyzed in the following order; PF,
TSC, SSIM
TS
, SSIM
RS
, a, b, c, rd. SEMCBCF using
the values Ontology4 for O, 10 for k, pred2 for PF,
TSC
Lin
for TSC, SSIM
S
for SSIM
TS
and SSIM
RS
, 0.1
for a, 0.1 for b, 0.8 for c, 2 for rd gives the best result.
SEMCBF is used for enhancing user-item matrix
in SEMCBCF. So the performance of SEMCBCF is
dependent to the performance of SEMCBF. Because
of that, in this evaluation phase, both the performance
Table 2: Parameters and Possible Values of SEMCBF.
Parameter Values
Ontology (O) Ontology1
Ontology2
Ontology3
Ontology4
Max. Recursive Depth (rd) 0,1,2,3,4,
5,6,7,8
Weight of TS (a)
Weight of RS (b) (a+ b+ c) = 1
Weight of AS (c)
Measure for Taxonomy TSC
CM
Similarity Between TSC
Wu&Palmer
Concepts (TSC) TSC
Lin
TSC
Mclean
SSIM Method for TS (SSIM
TS
) SSIM
1
, SSIM
2
,
SSIM
3
, SSIM
S
SSIM Method for RS (SSIM
RS
) SSIM
C
, SSIM
A
Number of Nearest 5,10,15,20,
Neighbors (k) 30,50,100,200
Prediction Function (PF) pred1, pred2
of SEMCBF and SEMCBCF are compared with some
other approaches. Table 3 gives precision, recall and
F-measure results of SEMCBF, SEMCBCF and some
approaches obtained from (Karaman, 2010). And also
CBCF (Melville et al., 2002) is implemented using
the same dataset to make a fair comparison. It can
be seen from Table 3, SEMCBF and SEMCBCF out-
perform the other approaches in recommending high-
quality items.
Table 3: Comparison of SEMCBF with Other Approaches.
Approach Prec. Rec. F-Measure
(%) (%) (%)
MovieLens 66 74 69,8
MovieMagician 61 75 67,3
Feature-Based
MovieMagician 74 73 73,5
Clique-Based
MovieMagician 73 56 63,4
Hybrid
OPENMORE 75,2 73,7 74,4
ReMovender 72 78 74,9
CBCF 60 95,2 73,6
SEMCBF 63,4 92,3 75,2
SEMCBCF 63,7 93,1 75,6
4 CONCLUSIONS
This paper presents a hybrid approach, which uses
both content-based and collaborative filtering, in or-
der to overcome the sparsity and item cold-start prob-
WEBIST 2011 - 7th International Conference on Web Information Systems and Technologies
370

lems of collaborative filtering. The presented ap-
proach is based on content-boosted collaborative fil-
tering presented in (Melville et al., 2002). Our hybrid
approach (SEMCBCF) first uses content-based filter-
ing (SEMCBF) to enhance the user-item similarity
matrix, then performs collaborativefiltering using this
enhanced user-item matrix. The contribution of our
approach is that it uses semantic similarity measures
on ontology-based metadata to calculate the similari-
ties of items in content-based filtering. Our hypothe-
sis was that using semantic similarity measures rather
than naive Bayesian classifier (Mitchell, 1997) which
is used in (Melville et al., 2002) will improvethe qual-
ity of recommendations.
In the evaluation phase, first, SEMCBF was
fine-tuned by determining the values of its parame-
ters. Then, using the determined values, SEMCBF
and SEMCBCF was evaluated. The results showed
that SEMCBF and SEMCBCF outperforms content-
boosted collaborative filtering presented in (Melville
et al., 2002) and some other approaches.
The characteristics of the ontology, such as the
taxonomy of concepts and representation of features
significantly effect performance of SEMCBF. For
further research, ontology refinement will be focused
to improve SEMCBF. And also, SEMCBF will be
improved by assigning some weight to relations and
attributes in the ontology.
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