Content-based Recommender System using Social Networks
for Cold-start Users
Alan V. Prando
1
, Felipe G. Contratres
2
, Solange N. A. Souza
2
and Luiz S. de Souza
3
1
Instituto de Pesquisas Tecnológicas, São Paulo, Brazil
2
Universidade de São Paulo, São Paulo, Brazil
3
Faculdade de Tecnologia, São Paulo, Brazil
Keywords: Recommender System, Social Networks, Cold-Start, Content-based Recommendations.
Abstract: Recommender systems have been widely applied to e-commerce to help customers find products to purchase.
Cold-start is characterized by the incapability of recommending due to the lack of enough ratings. In fact,
solutions for the cold-start problem have been proposed for different contexts, but the problem is still
unsolved. This paper presents a RS for new e-commerce users by using only their interactions in social
networks to understand their preferences. The proposed Recommender System (RS) applies a content-based
approach and improves the experience of new users by recommending specific products in a preferred
identified user category by analysing their data from the social network. Therefore, it combines three social
network elements: (1) direct user posts (e.g.: "tweets" from Twitter and "posts" from Facebook), (2) content
"likes" (e.g.: option "like" on a "post" or "tweet" posted by another user), and (3) page "likes" (e.g.: option
"like" on a Facebook page). The proposed RS was tested for a retail e-commerce, which usually not only has
a large range of categories of products, but also has products within these categories. The difficulty in
predicting a product increases sharply with a greater number of categories and products. According to the
experiment conducted, the proposed RS demonstrated to be a reasonable alternative to cold-start, i.e., for
users accessing e-commerce for the very first time.
1 INTRODUCTION
Data volume has grown continuously over the last
years. Companies started to store all sorts of data,
from server logs to any useful information with
business value (Landim et al. 2013) (Fan & Bifet
2013) (Singh & Singh 2012). As a consequence, our
ability to collect data from various applications in
different formats has been increasing drastically.
This data – and the technologies capable to deal with
them - is called Big Data (Madden 2012), which has
truly affected current business. A few years ago, an
enterprise used to store a restricted subset of data
containing only core information. In contrast,
nowadays an enterprise can leverage all the
information available with large data to gain insights
and make better decisions, having an advantage over
the market competitors (Singh & Singh 2012).
Recommender Systems are a prime example of
the mainstream applicability of Big Data. With
applications such as e-commerce and music/video
streaming, services use recommender systems
techniques to mine and to process large volumes of
data to better match the needs of their users in a
personalized fashion (Fan & Bifet 2013). Above all,
recommender systems have emerged as a way to
help users in their decision-making process because
they suggest the most suitable items to a particular
user (Adomavicius & Tuzhilin 2005). Recommender
system (RS) encompasses personalized algorithms
that use machine learning (ML) and data mining
techniques to identify the preference of each user
individually (Yang et al. 2011) (Ricci et al. 2011). It
is common for e-commerce to employ recommender
systems as a differential (Linden et al. 2003).
However, sometimes there is a lack of data and
the RS cannot predict user preference. This occurs,
for example, when a new user registers in the e-
commerce and the system may not have enough
information about him/her for recommending. This
problem is known as cold-start (Ricci et al. 2011)
(Adomavicius & Tuzhilin 2005) (Sun et al. 2015)
(Fernández-Tobías et al. 2016).
Prando A., Contratres F., Souza S. and de Souza L.
Content-based Recommender System using Social Networks for Cold-start Users.
DOI: 10.5220/0006496301810189
In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KDIR 2017), pages 181-189
ISBN: 978-989-758-271-4
Copyright
c
2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
This paper introduces a RS for e-commerce by
using only social network data to improve
recommendations for the cold-start problem. More
specifically, predicting products for the user to buy
is solely made by using posts from users’ social
networks. As a result, the RS uses techniques that
correlate e-commerce products to the elements of
social networking. Thus, even if there are no explicit
or implicit ratings the data extracted from the social
networks may be sufficient to determine user
preference.
This paper is organized as follows. Section 2
presents the main aspects of previous work on
Recommender Systems (RS). Section 3 introduces
the recommender system definition, besides
including the main characteristics of social
networks. Section 4 details the proposed RS.
Section 5 presents the experiment implemented to
evaluate the proposed RS and its results. Finally,
section 6 concludes it and suggests future work.
2 RELATED WORK
A mix of algorithms to obtain implicit and explicit
evaluations or to infer user action have been
suggested in previous work (Schein et al. 2002)
(Burke 2007) (Konstan & A. 2004) to solve the
cold-start problem. Despite the efforts made, the
lack of information and ratings are still a problem
(Ricci et al. 2011) (Fernández-Tobías et al. 2016).
Fernándes-Tobías et al. (Fernández-Tobías et al.
2016) proposes colllaborative-filtering (CF)
techniques (Adomavicius & Tuzhilin 2005) (Burke
2007) (Ricci et al. 2011) (Kaššák et al. 2016) that
include personality information and cross domain
information to recommend items to new users. Their
proposal assumes that, in certain domains, users with
simliar personalities tend to have similar
preferences. They employ the Big Five test
(Quirino, Mals, Groterhorst, De Souza, et al. 2015),
a personality test to outline the profile of a person, to
define users’ personality trait and the system thus
has information about the users’ pesonalities.
Moreover, based on personality traits, they propose
the CF technique to extract user preferences in the
auxiliar domains and to apply this information to
recommend itens in a target domain to a new user.
Good results were derived with this personality-
aware method. However, new users have to take
the Big Five test before interacting with the
recommender system, which, in a real case, may
discourage them to keep using e-commerce.
Considering the lack of data, social networks
appear as natural sources of data for
recommendation and a feasible solution for cold-
start. In recent years, different works have employed
data from social network to recommend products
and as a solution for cold-start.
Ma et al. (Ma et al. 2011) proposes a method that
forms a social composition, according to the
common interests among the friends of a particular
user. Following the same idea, a recommendation is
made for a particular user based on the social
influence of both close and distant friends (He &
Jianming 2010). Oliveira et al. (Oliveira et al. 2012)
built a RS based on trust among friends, called trust-
aware recommender systems. In the same fashion,
but considering the cold-start problem, Caron, and
Bhagat (Caron & Bhagat 2013) use the information
of a new user’s friends in e-commerce and propose a
learning model of user preference in the social
network. Lalwani et al. (Lalwani et al. 2015) assume
users that are part of a community have similar
preferences, or are influenced by it. They utilize
social interactions (friend connections) to detect
Facebook communities and to simulate cold-start
scenarios, recommending items already ranked by
users in the community to the user without an
assessment history.
Maniktala et al. (Maniktala et al. 2015) also
assume users prefer items already acquired by
friends, but the friends’ influence depends on how
strong the friendship relation is. They propose
techniques to classify social relationships in strong
or weak and recommend items acquired by users
with strong friend connection to a new user.
However, they consider cold-start for a user that has
consumed up to 5 items, which can be considered a
moderate cold-start once there is some information
about the user.
Felicio et al. (Felicio et al. 2016) use models
already built in systems for users to suggest items to
a new user. They built a personalized model for a
cold-start user by selecting prediction models from a
set of strongly linked users. They handle several
social network connection weight metrics to classify
links among users.
Amatriain (Amatriain & Xavier 2013) presents
the main algorithms used in RS. Zhang and
Pennacchiotti (Zhang & Pennacchiotti 2013b)
(Zhang & Pennacchiotti 2013a) employ data mining
techniques to extract information from social
networks to find a users’ preferred categories in e-
commerce. In their experiment, classes of goods
from Ebay, which were previously ranked by the
user, are associated with a class of user-liked pages
(user explicitly indicates the interest in a determined
Facebook page). In this experiment, specific
products in categories are not considered.
The data-mining field has considerably advanced
in recent years due to technological advances
providing the processing and storage of a large
volume and variety of data. In particular, social
networks are considered essential to this change,
driving the creation of such data, which is generated
by different users. Thus, individual efforts regarding
techniques for extracting information on specific
data types, such as text mining, become relevant for
achieving results (Aggarwal & Zhai 2012) (Hu &
Liu 2012) (Xia et al. 2016).
Hu and Liu (Hu & Liu 2012) introduce a text-
mining model for social network divided into three
stages: (1) Text process: incoming documents are
manipulated to achieve appropriate representation –
stop-word removal and stemming are some of the
techniques employed. (2) Text representation:
documents are transformed into sparse numeric
vectors (bag-of-word, BOW, or vector space model,
VSM) – an algebraic model for representing text
document as a vector of identifiers (ex. index terms),
in which the relevance ranking of documents may be
calculated. Term Frequency-Inverse Document
Frequency (TF-IDF) is a common technique to
weight each term in a document, scoring the
importance of the words in a document based on
how frequently they appear across multiple
documents. (3) Knowledge discovery: having
documents represented by a vector - ML algorithms
can be applied to find document similarities.
Similarly to Zhang and Pennacchiotti (Zhang &
Pennacchiotti 2013b) (Zhang & Pennacchiotti
2013a), our proposed RS uses data extracted from
users’ social networks, but in our case, no previously
ranked purchase or evaluations given by users are
employed. In other words, our experiment considers
that users are really new in the e-commerce and no
information about them exists, which is classified as
an extreme cold-start scenario (Fernández-Tobías et
al. 2016). Moreover, differently from most of the
previous works, our RS does not use users’ friends’
data. Therefore, the proposed RS contributes by
showing that a content-based approach
(Adomavicius & Tuzhilin 2005) (Burke 2007) (Ricci
et al. 2011) (Kaššák et al. 2016) may also be
employed, yielding good results. Our RS
investigates whether data posted by the user himself
may be used as a complement to other techniques to
make more assertive recommendations.
Despite the variety of data in social networks,
text is clearly predominant. Therefore, the proposed
RS applies text mining techniques and ML
algorithms to perform recommendations by
correlating user social interactions with e-commerce
products, characterizing a content-based
recommendation.
3 RECOMMENDER SYSTEM
AND PROBLEM STATEMENT
Formally, recommendation problems can be
formulated as: C is the set of all users and S is the
set of items that might be recommended. Utility
function u measures how useful item s is to a
particular user c, e.g., u: C x S R, where R is an
ordered set. Hence, for each user c C, an item s
S that maximizes the utility to user c is searched;
this is represented by equation 1 (Adomavicius &
Tuzhilin 2005):
(1)
In RS, the utility of an item is represented by a
rating, which points out the interest of a user in a
particular item. Each user in C has many attributes
such as age, gender, marital status, etc. Similarly,
each item in S is defined by its characteristics such
as description, specifications, etc. Therefore, the
proposed RS is content-based, since for item s and
user c, the function u(c, s) estimates the rating given
by user c to item s, based on utilities u(c, s
i
) assigned
by user c to items s
i
S, which are similar to item s.
Accordingly, to solve the cold-start problem, the
proposed RS uses data mined from users social
networks (e.g., recent social data, such as tweets and
posts) as implicit ratings to calculate u(c, s).
3.1 Social Networks
The last decade represented a revolution in the way
society interacts due to the intense use of social
networks. This interaction has become a powerful
tool for analysis and knowledge discovery regarding
its users and the way in which they communicate
with each other. Social networks are a type of
service offered by the web that allows its users to
exchange information (Quirino, Mals, Groterhorst,
de Souza, et al. 2015) (Albalooshi et al. 2012).
Recently, social networks such as Facebook, Twitter,
LinkedIn and Foursquare became extremely popular
in the whole world. The number of Facebook users
increased 20 times in the 2008 - 2012 period (Long
Jin et al. 2013). In additon, a large number of
different kind of devices such as desktops, notebooks
mobile, smartphones and tablets have been
facilitating the use of social networks (Long Jin et al.
2013).
Social networks intend to provide security an
privacy for its users (Joshi & Kuo 2011). Each social
network has different approaches to deal with user
privacy. For example, Twitter exposes all posts
published, whereas LinkedIn restricts data accessed
according to the type of user account and
relationship. Facebook makes available an
Application Programming Interface (API) to other
applications to connect to it and the number of
systems, which allow their users to register or to
connect to them by using a Facebook account, have
increased. If an user opts for it, the application
recovers the user’s personal data from Facebook,
such as name, date of birth, etc. In general, only
personal data is transmitted to the applications from
Facebook, but if the user gives permission, other
information can be accessed.
The social networks employed as data source to
the proposed RS was Facebook and Twitter because
of their popularity, considering the number of users
(Quirino, Mals, Groterhorst, de Souza, et al. 2015)
(Derczynski et al. 2015). Besides, the access to
Twitter is free, facilitating the access of user data,
although only the last 200 tweet users are acessible.
In case of Facebook, an a application was created to
allow accessing the users data.
4 CHARACTERISTICS OF
PROPOSED RECOMMENDER
SYSTEMS
4.1 Recommendation Process
Figure 1: Flowchart of recommendation.
The recommendation process is divided into two
main flows (Fig. 1): (1) RS batch process, which runs
only once a day (or when a new product is included);
(2) RS online process, which runs on each
recommendation request. To our knowledge, this is
the first study that creates a process (Fig. 1) able to
recommend using only users’ social network data to
match products from real e-commerce employing
content-based approach. Although the techniques
employed have already been discussed in the Text
Mining and Information Retrieval research areas, the
combination of these techniques to achieve a
content-based recommendation by solely using social
networks data can be considered the main
contribution of this work.
According to Fig. 1, the tasks performed are:
a) Consistency and representation of product
terms and social network data:
First, the consistency step applies the stemming and
stop word removal techniques (Hu & Liu 2012) to
remove generic terms and normalizes both social
media data and product data. Moreover, the
representation step also calculates TF-IDF (eq. 2) to
prepare the data for the next task.
TF-IDF is applied in batch and in an online
process. More formally, in the batch process, TF-
IDF (eq. 2) is represented by a set of product
features S with a list of terms t representing each
word of the feature.
(2)
Where:
is the total of terms in product s.
is the total of terms in product s.
is the number of products with term t.
is the total of products.
As a consequence, a Vector Space Model (VSM)
represents all the products with terms weighted for
each product. The VSM is used in the training task.
Similarly, in the online process, TF-IDF uses a
set of social data (posts) of user , with a list of
terms representing each word of (eq. 3).
(3)
Where:
is the total of terms in
social data p.
is the total of terms in social data
p.
is the number of social data with term t.
is the total of products.
Therefore, all social data are represented by a
VSM allowing the computation of the similarity
distance between social data and product.
b) Training of Classifier using product terms as
input and category as classes:
The training phase starts as soon as RS is available.
It receives the VSM of product features as input and
the product categories as classes. Therefore, after
training, the classifier is used to determine the
category of products the social data belong to. When
the training phase is completed, the RS is ready to
receive requests from e-commerce. Each request
uses the classifier in isolation with the VSM of
social data to predict the preferred user category.
Since each social datum derives a product category,
a function named classrank (eq. 4) is employed for
ordering the preferred classes of user c, with f
representing a set of categories.
(4)
Where:
is the total of social data from
user classified in class , establishing the order,
according to eq. 5:
(5)
In summary, eq. 5 orders the categories by its
importance, which is measured by the number of
times a social data is related to.
c) Acquisition of user social networks data
Not all data from social networks are available or
can be used to determine user preferences. Thus, the
data from social network used by the classifier in the
proposed RS are:
Likes on pages (from Facebook). Social networks
establish pre-set categories that can be related to
e-commerce categories. Zhang and M.
Pennacchiotti (Zhang & Pennacchiotti 2013b)
also used this approach.
Likes on contents (from Facebook and Twitter)
are used in the classification and product
similarity phases. The content can have a text
format employed by users to express their opinion
to friends (Albalooshi et al. 2012) (Long Jin et al.
2013).
Post (from Facebook) and “Tweets” (from
Twitter) are used in the classification and product
similarity phases. Both are texts published by
users to express their opinion to friends
(Albalooshi et al. 2012) (Long Jin et al. 2013).
Shares (from Facebook and Twitter), which are
also utilized by users to express their opinion to
friends (Albalooshi et al. 2012) (Long Jin et al.
2013) (Fersini et al. 2016) (Saif et al. 2016).
The intention is to perform a more personalized
recommendation than the one proposed by Zhang
(Zhang & Pennacchiotti 2013b). For instance, a RS
for sportswear e-commerce, in the case of a user
liking a football page and publishing a post with the
word “Barcelona", a list called "Football" with
products related to the Barcelona Club will be
recommended.
In the same example, Zhang recommends (Zhang
& Pennacchiotti 2013b) a list called "football", but
no products related to Barcelona will necessarily be
recommended.
d) Similarity between social network data and
products in the classified category:
The preferred categories of users are obtained from
the data extracted from their social network.
However, since each category has a large dataset of
products, it is hard to find what product is more
similar to the data produced by the user social data
in a category. In order to provide the best
recommendation, the cosine similarity (Adomavicius
& Tuzhilin 2005) (Amatriain & Xavier 2013)
(Amatriain 2013) is applied to each product
(between each vector P - representing a particular
product, and vector S - representing the user social
data recovered), yielding an ordered list of the
products most similar to the user social data.
4.2 Supervised Machine Learning
Algorithms Employed
The architectures employed is modular and
extensible and is an extension of (Prando & Alves de
Souza 2016). RS should be able to obtain user social
data and to process the recommendation in a suitable
time for e-commerce to use it, i.e., during the visit of
the user to e-commerce (Amatriain & Xavier 2013).
Big Data technologies are employed in proposed RS
(Fan & Bifet 2013) (Rios & Diguez 2014), (Lin &
Ryaboy 2013), which could be scaled to reach e-
commerce response time requirements.
The proposed Recommender systems use Naïve
Bayes, Decision Tree and SVM (Support Vector
Machine), supervised ML algorithms, to classify
products by their characteristics, representing the
content-based recommendation approach (Rokach &
Maimon 2007) (Joachims & Thorsten 2002) (Rennie
et al. 2003).
The classifiers utilized in the experiment were
dictated by the technologies employed, namely
Apache Spark and Apache Hadoop. Consequently,
only Naïve Bayes and Decision Tree were used
because SVM multiclass was not found in Apache
Spark when the experiment was carried out.
In order to define the classifier to be employed,
Naive Bayes or Decision tree, two variables were
observed: (1) prediction response time, and (2)
performance – returned success rates by the
classifier for an 80/20 comparison, in which 80% of
a product data is employed in the training phase and
20%, in the assessment phase. The categories of
product tree are handled as classes for the classifiers.
In Fig. 1, flows (1) and (2) of the proposed RS are
strongly affected by the hierarchic level chosen.
Classifier performance in flow (1) is affected as
follows: category in a higher level has more
products in it, increasing the class features for
training – contributing to achieving better
performance (e.g. the “TV and Home Theatre” root
category contains the whole TVs and home theatres
while the “LED TV” subcategory only has LED TVs
of the e-commerce). On the other hand, in flow (2),
computational complexity and the recommendation
time processing increase the higher the category
level, because this implies more products in a class.
Consequently, more calculation is conducted in the
similarity cosine phase. The category of social data
in the e-commerce context is predicted in the social
data classification phase, Fig. 1 - flow (2). Then, in
the next similarity cosine phase, this social data are
compared with all the products of the category
identified, allowing products more similar to social
data to be recommended.
Table 1 shows the results of Naïve Bayes (NB)
and Decision Tree (DT) classifiers evaluation, using
two entries: (1) VSM using root categories as a
class; (2) VSM using second level categories as a
class. As a result, a more appropriate response time
and performance to process a task for a
recommendation during an interaction on an e-
commerce were achieved with Naïve Bayes.
Table 1: Evaluation of Naïve Bayes and Decision Tree
classifiers.
Algorithm
Prediction
time (ms)
Performanc
e 80/20
NB - root categ. (NBC)
51
0.96
NB – 2º level categ.
37
0.68
DT - root categ.
120
0.53
DT – 2º level categ.
124
0.09
In summary, the classification process can be
detailed as follows: the classifier is trained using a
VSM, which is calculated by employing TF-IDF of
the description of each product, having classes
represented by product categories. This way, the
NBC classifier (Table 1) learns the terms of products
distributing them into categories, allowing the
classification of any other set of terms with similar
representation. Hence, with the model trained, RS is
ready to receive requisitions, which are social data
retrieved and represented by VSM of terms using
TF-IDF. Since each social datum has its own
representation, it might be classified in a product
category, concluding the classification process.
5 EXPERIMENT
In order to test the proposed RS and to show the
results, an application was created to simulate a
user’s first time in e-commerce. This application
was employed because of the available budget. The
use of a real scenario will demand a more robust
infrastructure than the one used. For the experiment,
1.449 products divided into 239 categories were
used, arranged into 15 categories in the first level
(root category) and 67 in the second level (daughters
of root category). These products are a subset of the
products extracted from the e-commerce BestBuy by
using an API available on the web.
Figure 2: Form: assessment of products.
The steps of the application created are:
1. The user must accept to participate in scientific
research, having prerequisites: legal age and
being registered in Facebook and/or in Twitter.
2. The user logs in by using Facebook or
informing his/her Twitter account. Therefore,
user data is obtained from the social networks
Facebook and Twitter.
3. The recommendation flow starts and products
are shown.
4. The user must rate the products recommended
by RS, assigning a score of 1 (no interest) to 5
(high interest). The value attributed to the
recommendation by the user is employed as
metrics in the RS assessment phase (Fig. 2).
5. User submits the evaluations.
The application was distributed to people
selected in advance. The authors invited people
(friends, colleagues, students at university) to take
part in the experiment. To conduct the controlled
acceptable experiment, a minimum of 68 individuals
or participants is necessary, which allows estimates
with an error-margin of 10% and confidence level of
90% (Krejcie & Morgan 1970).
Experiment data were organized in 4 text files:
users, products, social_data and assessment. Table 2
shows file name, its structure (file fields) and an
example of data for each field.
Table 2: Structure of files with data sample of experiment.
Users File
name
user.txt
structure
User ID; gender; age; source
data sample
1; M; 28; Facebook
Products File
name
product.txt
structure
product ID; name; description; category
data sample
1; iphone; apple iphone; smartphone;
Social Data File
name
social_data.txt
structure
user ID; BOW of data
data sample
1; smartphones, nice; eat, past;
Assessment File
name
Assessment.txt
structure
user ID; product ID; assessment;
data sample
1; 1; 3
5.1 Experimental Results and
Assessment
Individuals that participated in the experiment did
not receive any advertisement regarding its content
or goal, and totalled 98 participants. In summary, the
following results were achieved:
It was impossible to generate recommendation
for 16 participants due to the lack of social data
or the NB classifier could not predict the class of
the social data captured.
72 participants finished the whole process.
718 recommendations were generated and rated.
The result of cosine similarity yields values in
the [-1, 1] interval. All the results below 0.02 were
discarded, this range of values being considered to
represent no similarities between users’ social data
or product features. Then, the distribution of cosine
similarity results from 0.02 (smallest) to 0.7
(highest). 65% of the results are observed to be
between 0.02 and 0.2.
Furthermore, the participants rated each
recommendation given by the proposed RS (Fig. 2)
with grades from 1 to 5. To evaluate performance,
the root mean square error (RMSE) (Shani &
Gunawardana 2011) is used to measure the
difference between both ratings. The use of RMSE is
very common in the field and it makes a good error
metric for RS (Ricci et al. 2011) (Fernández-Tobías
et al. 2016) (Amatriain 2013). To calculate the
RMSE between users and RS ratings, firstly RS
ratings need to be normalized because they generate
a numerical value in the interval of [-1, 1], whereas
user ratings attributes values in the interval of [1, 5].
Therefore, RS results were normalized to values in
the range of [1, 5] (eq. 6). In equation 6, let P and S
be the set of products and social data of users,
respectively. Let
be the product returned by RS
to user
.
(6)
Where:
is the RS rating for
social data of user i in product
and
is the
social data related to product
.
It is now possible to calculate the RMSE (eq. 7).
Let
be the grades attributed by user u and
RS to product p, respectively.
(7)
Where:
is the total recommendations made.
is the total of users.
.
Considering equation 7, the RMSE value can
vary from 0 (best case, i.e., the grades given by the
user and cosine similarity are much closer) to 4
(worst case, i.e., the grades given by user and cosine
similarity are much more distant). Ranges are thus
defined to interpret RMSE results, such as: [0, 1] –
very good; (1, 2] - good; (2, 3] - bad; (3, 4] - very
bad. Finally, the result for RMSE was 1.71.
According to the ranges defined, the RS performed
well and can be considered a good alternative to the
cold-start.
Complementing the results from the RMSE,
Table 3 shows the numbers of occurrences in which
grades given to the product offered by the proposed
RS and users are equal. According to this result, it is
possible to affirm that proposed RS has 40%
accuracy. This reveals that user’s social data, i.e.,
data extracted from the social network of a user,
who is receiving the recommendation, is an
important source of data for recommendation
processes, mainly for the cold-start problem, in
which no data about the user exists. Besides, the
experiment was conducted for a more generic e-
commerce than those of movies or music,
confirming the efficiency of the proposed RS.
Table 3: Correspondence between grades given by the
proposed RS and attributed by the user.
Difference between grades
attributed by the proposed
RS and users
Number of
occurrences
Percentage
0
288
40.11
> 0
430
59.89
6 CONCLUSIONS
To demonstrate how the proposed RS responds to
the cold-start problem, an experiment that
recommends products by reproducing the conditions
of an e-commerce to a new user was built and
distributed to a group of participants. As a result, a
satisfactory RMSE of 1.71 was scored. Additionally,
the results were complemented by counting the
number of occurrences, in which the grades given by
user and proposed RS coincided, showing that the
proposed RS had 40% accuracy. It demonstrates
that the proposal can be a good alternative to the
cold-start problem. Besides, the proposed RS
employed a content-based approach and the results
showed that this technique is appropriate to cold-
start and, if it is used as a complement to the
collaborative-filtering approach, it may even further
improve the results from a cold-start problem.
There are many future important challenges in
RS using social networks that arise from the nature
of personalization: discovering user preferences by
processing a sparse, diverse and large dataset
employing techniques that demand a high
computational capacity. As continuity of this work
is intend to: (i) use sentiment analysis technique to
evaluate the sentiment represented in social data
before use it in recommendation process (ii)
encompass data from social network of friends
network, i.e., not only use data from user social
network, but also from his/her network of friends.
ACKNOWLEDGEMENTS
Authors are grateful for the support given by São
Paulo Research Foundation (FAPESP). Grant
#2014/04851-8, São Paulo Research Foundation
(FAPESP).
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