A Literature Review of Recommender Systems for the Cultural
Sector
Nguyen Anh Khoa Dam
a
and Thang Le Dinh
b
Department of Marketing and Information Systems, Université du Québec à Trois-Rivières, Trois-Rivières, Canada
Keywords: Recommender System, Cultural Sector, Business Analytics, Artificial Intelligence, SMEs/SMOs.
Abstract: Nowadays, organizations in the cultural sector have faced the problem of improving the discoverability of
their products to meet the target objective regardless of the tremendous amount of information. In this respect,
recommender systems have been proven to be the solution for enterprises, especially for cultural small and
medium-sized organizations and enterprises (SMOs/SMEs), to enhance the discoverability of their products.
This study aims at presenting a concept-centric literature review of recommender systems for cultural
SMEs/SMOs to identify the current status-quo of the application in six cultural domains, including heritage
and libraries, live performance, visual and applied arts, written and published works, audio-visual and
interactive media, and sound recording. The finding of this paper reveals the adoption of recommender
systems of cultural SMOs/SMEs is still in the early stage of maturity. The specific status-quo of recommender
system adoption in each cultural domain is uncovered through the literature review. Other relevant aspects,
which relate to data sources, data mining models, and algorithms, are also discussed in detail. Finally, the
paper proposes future research directions to promote the application of artificial intelligence in general, and
recommender systems, in particular, in the cultural sector.
1 INTRODUCTION
In the age of big data, the challenge of customers has
changed from information shortage into information
overload (Sassi, Mellouli, & Yahia, 2017). This
stimulates the need of filtering out misleading
information and efficiently searching for the right
information (Ekstrand, Riedl, & Konstan, 2011;
Schafer, Frankowski, Herlocker, & Sen, 2007; Wedel
& Kannan, 2016). Accordingly, organizations and
enterprises in the cultural sector face the problem of
improving the discoverability of their products and
services; hereafter called cultural products, to meet
target profits regardless of the tremendous amount of
information. The discoverability (Dasgupta et al.,
2007) is defined as the ability of cultural products to
be found by customers who search for them and to be
recommended to those who are unaware of them.
In this respect, recommender systems (RSs) have
been proven to be the solution for the cultural sector,
especially for small and medium-sized organizations
(SMOs) and enterprises (SMEs), to enhance the
discoverability of their products (Bartolini et al.,
a
https://orcid.org/0000-0003-0928-8402
b
https://orcid.org/0000-0002-5324-2746
2016; Kabassi, 2013; A. Moreno, Valls, Isern, Marin,
& Borràs, 2013). However, choosing and developing
the right approach for RSs seem to be a challenge for
most enterprises, especially in the case of SMOs and
SMEs with limited resources (Ekstrand et al., 2011;
Wedel & Kannan, 2016).
For this reason, this study aims at presenting a
literature review on recommender systems in the
cultural sector for SMEs/SMOs. Therefore, the
primary objective of the study is to identify the
current status-quo of the RS adoption in different
domains of the cultural sector. Based on the primary
objective, the second objective is to identify the
relevant aspects, which need to be taken into account,
relates to data sources, data mining models, and
algorithms for recommender systems.
This paper is structured as follows. At first, the
paper continues with the theoretical background. The
research design, including the research process and
classification framework, is also presented as the
foundation to categorize articles in this literature
review. Then the paper analyzes the reviewed articles
with an in-depth discussion. Future research
Dam, N. and Dinh, T.
A Literature Review of Recommender Systems for the Cultural Sector.
DOI: 10.5220/0009337807150726
In Proceedings of the 22nd International Conference on Enterprise Information Systems (ICEIS 2020) - Volume 1, pages 715-726
ISBN: 978-989-758-423-7
Copyright
c
2020 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
715
directions are inferred from the discussion. Finally,
the paper concludes with theoretical and practical
contributions concerning this research stream.
2 BACKGROUND
The theoretical background begins with the different
types of RSs, continues with the relations between
RSs and customer experience and satisfaction, and
ends with the adoption of RSs in the cultural sector.
Types of RSs. Recommender systems can be
classified into three types: Collaborative filtering
(CF), Content-based filtering (CB), and Hybrid
system (Felício et al., 2016; Garcia, Sebastia, &
Onaindia, 2011). A CF recommender system requires
data on users’ evaluation of purchase history to make
suggestions (Lin, 2014). In contrast, CB
recommender systems connect profiles of users'
preferences with descriptions of relevant items
(Mathew, Kuriakose, & Hegde, 2016). The hybrid
systems integrate CF techniques with CB techniques
or even with other techniques to optimize
recommendations (Li, Xu, Wan, & Sun, 2018).
RSs and Customer Experience. Recommender
systems play an important role in optimizing
customer experience from finding to engaging with
the products (Albadvi & Shahbazi, 2009; Barragáns-
Martínez et al., 2010). The most significant function
of RSs is to predict pertinent services or products
based on users’ preferences (Lu, Wu, Mao, Wang, &
Zhang, 2015; H.-R. Zhang & Min, 2016). To put it
concisely, RSs have two key functions: to predict and
to recommend the most relevant service or products
(Ekstrand et al., 2011; Schafer et al., 2007).
RSs and Customer Satisfaction. Recommender
systems have been proven as a means to increase
customer satisfaction, maintain a long-term
relationship, and improve financial performance
(Briguez et al., 2014; Siu, Zhang, Dong, & Kwan,
2013). Particularly, the applications of RSs produce
prosperous results in the field of cultural heritage,
tourism, and leisure activities (Bartolini et al., 2016;
Kabassi, 2013; A. Moreno et al., 2013). Movie and
music RSs built by Netflix and Yahoo have
noticeably improved the financial performance of
these enterprises (Christensen & Schiaffino, 2011;
Jannach, Resnick, Tuzhilin, & Zanker, 2016).
RS Adoption in the Cultural Sector. Regardless of
a wide range of adoption of RSs in the cultural sector,
studies on such research stream scatter all over the
literature. As a consequence, it is challenging to take
an overview of the big picture of the RS adoption
among different cultural domains. Even though CF
systems are proven to be effective for the
entertainment and cultural domain, they raise issues
related to user engagement and accuracy as
recommendations inferred from a community may
not be precise for an individual member (Kabassi,
2013; Villegas, Sánchez, Díaz-Cely, & Tamura,
2018). Another drawback of the CF approach is the
insufficient amount of data input, which is defined as
the cold start, impedes many SMEs/SMOs at the
early stage of adoption (Kabassi, 2013; Park, Kim,
Choi, & Kim, 2012). On the other hand, CB systems
face the problem of identifying algorithms for
effectively matching common attributes between
users and items (F. Deng, Ren, Qin, Huang, & Qin,
2018; Yao, Sheng, Segev, & Yu, 2013). The hybrid
approach combing CF and CB has shown to
outperform (van Capelleveen, Amrit, Yazan, & Zijm,
2019); yet, it requires a certain level of investment for
information technology (IT) infrastructure which can
be an obstacle for most SMEs/SMOs with financial
constraints (Barragáns-Martínez et al., 2010; Park et
al., 2012; Yao et al., 2013).
3 RESEARCH DESIGN
3.1 Research Process
As the nature of research on recommender systems
and the cultural sector spreads over various databases,
this paper builds its literature review from a wide
range of academic sources such as Science Direct,
Emerald, Business Source Premier, EBSCOhost,
ProQuest, Google Scholars, and IEEE/IEE Library
(Park et al., 2012). The search process is conducted
through different descriptors: “recommender
systems”, “recommendation systems”, “cultural
domain”, “cultural sector”, “cultural heritage”,
“movie recommender”, “music recommend*”, “book
recommend*”, “image recommend*”. Then more
than 500 articles were screened based on abstracts,
structure, and content. As the paper focuses on
SMEs/SMOs, articles with relevant content are
primarily chosen. Considering the limited number of
articles for cultural SMEs/SMOs, the search was
expanded to cultural organizations in general to
ensure the broad literature review. However, the
content of 69 selected articles is screened with the
attention on the data mining models and IT
infrastructure, which are applicable for SMEs/SMOs.
These selected articles are from top Management and
Information Systems journals such as Expert Systems
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with Applications, Decision Support Systems,
Knowledge-Based System, Decision Support
Systems, IEEE, Information Sciences. These journals
are also listed in the Scimago Journal & Country
Rank in 2018.
Table 1: Distribution of articles by journal titles.
Journal Title No %
Expert Systems with Applications
IEEE
Information Science
Knowledge-Based System
Decision Support Systems
Neuro Computing
Others
17
11
3
6
4
2
26
24.6 %
15.9%
4.3%
8.7%
5.8%
2.9%
37.7%
Total 69 100%
According to Table 1, the number of relevant
articles comes from Expert Systems with
Applications (24.6%), followed by IEEE (15.9%) and
Knowledge-Based System (8.7%). During the
searching process, the forward and backward search
techniques are implemented to ensure that the review
of 69 chosen articles can represent the literature of
this domain. Therefore, the paper can demonstrate its
validity and reliability as a literature review.
The 69 reviewed articles are also categorized by
years of publication (Figure 1). The period of a
decade is chosen to examine the status quo and trends
of research in this field. It is noticeable that the
number of articles considerably increases compared
to the past 10 years. There is a substantial increase in
2016 and 2018. This indicates the interests of scholars
in the application of RSs in the cultural sector.
Figure 1: Distribution of articles by years of publication.
3.2 Classification Framework
The classification framework for recommender
systems in the cultural sector consists of different
domains and types of recommender systems. This
study is based on the previous study of Park et al.
(2012) whose objective is also to find out the trend of
RS adoption in various application fields.
Accordingly, the paper classifies reviewed articles
into six cultural domains and three types of
recommender systems. The classification of the
literature review is introduced in Figure 2. A detailed
description of the framework is presented in the next
part of this paper.
Figure 2: A classification framework.
Classification of Cultural Domains. Regarding the
classification of the cultural domains, the paper
classifies research articles based on the six domains
of the cultural sector according to the Canadian
Framework for Culture Statistics
(Canada, 2011).
These domains are Heritage and libraries, Live
performance, Visual and applied arts, Written and
published works, Audio-visual and interactive media,
and Sound recording. The Heritage and libraries
domain consists of definable related industries,
products, and occupations associated with archives,
libraries, cultural heritage, and natural heritage. The
Live performance domain covers activities
concerning the performing arts of theatre, artists, and
multidisciplinary events (e.g.: celebrations and
festivals). The visual and applied arts domain
contains the following sub-domains: original visual
art (e.g.: paintings, sculptures), art reproductions
(copies of original visual arts), photography, and
crafts. The written and published works domain
symbolizes different kinds of publications, including
books, periodicals, newspapers, and other published
works. The audio-visual and interactive media
domain is divided into three categories: film and
video, broadcasting (e.g.: radio, television, the
Internet) and interactive media (e.g.: online games).
The Sound recording domain represents sound
recording and music publishing.
Various RSs have been developed in different
domains of the cultural sector. The cultural heritage
domain has witnessed the evolution of various
versions of RSs in enhancing access to museums,
galleries, and other historic places. In the domains of
movies, music, and books, several RSs were built to
help users find relevant items. However, it is
A Literature Review of Recommender Systems for the Cultural Sector
717
challenging to find studies, which discuss RSs for
several different domains of the cultural sector.
Classification of RSs. As mentioned in previous
sections, recommender systems can be classified
generally into three specific types: collaborative
filtering, content-based filtering, and hybrid system.
Classifying the selected articles with types of
recommender systems indicates the current status and
trends of applications of such systems in different
domains of the cultural sector. The identification of
RS types also gives an idea of algorithms as well as
required IT architecture for developing and exploiting
the system. Therefore, it is significant to classify the
literature review based on the three types of
recommender systems. A more detailed description of
the three RSs is also presented as follows:
(1) Collaborative filtering (CF) recommender
systems are based on users’ evaluation of a set of
items such as songs, movies, images (Barragáns-
Martínez et al., 2010; Schafer et al., 2007). Under this
approach, users who share similar opinions on
specific items tend to have the same idea on other
issues (Barragáns-Martínez et al., 2010; Bobadilla,
Ortega, Hernando, & Gutiérrez, 2013). Collaborative
filtering recommender systems make predictions to a
specific user regarding the fact that these systems use
information extracted from many users (Schafer et al.,
2007; Wedel & Kannan, 2016).
(2) Content-based filtering (CB) recommender
systems match profiles of users’ preferences with
descriptions of relevant items (Barragáns-Martínez et
al., 2010; Yao et al., 2013). In these systems,
algorithms recommend similar items that a user used
to like or interact with. Three components of content-
based filtering recommender systems are profiles of
users' preferences, item descriptions, and item
comparison to user profiles (Barragáns-Martínez et
al., 2010). Content-based filtering recommender
systems seem to prevail today due to the vast content
on social media (Barragáns-Martínez et al., 2010;
Bobadilla et al., 2013).
(3) Hybrid recommender systems combine
content-based filtering with collaborative filtering or
other approaches to take advantage of each technique
(Barragáns-Martínez et al., 2010; Bobadilla et al.,
2013). The limitation of CF recommender systems
related to the lack of information about user’s
behaviors can be solved by the functionality of the CB
approach (Albanese, d'Acierno, Moscato, Persia, &
Picariello, 2011). On the contrary, the issue faced CB
recommender systems are over-specialization, which
only focuses on similar items to user preferences and
ignores "different" items although a user might want
to try something new (Borràs, Moreno, & Valls,
2014). As a matter of fact, the functionality of the CF
approach can overcome this limitation (Kazienko &
Kolodziejski, 2006). The hybrid approach is proved
to outperform the CF and CB ones (Yao et al., 2013).
4 LITERATURE REVIEW
Articles are categorized based on the classification
framework for recommender systems in the cultural
sector (Table 2). The degree of coverage of all the
articles related to each sub-category is noted with
three levels: highly covered (***), moderately
covered (**), and slightly covered (*) (Rickenberg,
Neumann, Hohler, & Breitner, 2012).
The reviewed articles are ordered in a concept-
centric way instead of being arranged based on author
names or year of publication (Webster & Watson,
2002). Accordingly, there are two key concepts:
cultural domains and RS types. On the horizontal
dimension of Table 2, the concepts are broken down
into units of analysis (Webster & Watson, 2002). The
“cultural domains” concept consists of six units of
analysis whereas the “RS types” concept covers three
units. On the vertical dimension of Table 2, the
cluster analysis approach (Rickenberg et al., 2012) is
applied to group similar articles belonging to the
same cultural domain. Reviewing the paper through
vertical clusters with horizontal units of analysis
reveals the relationships and coherency among
articles.
Cultural Domains. The number of reviewed articles
scatters over the six cultural domains as follows:
heritage and libraries (23), live performance (3),
visual and applied arts (11), written and published
works (18), audio-visual and interactive media (30),
and sound recording (28). Each cultural domain is
considered as a unit of analysis in which the degree
of intensity is calculated as the weighted total based
on the extent of coverage such as highly covered
(***x3), moderately covered (**x2), and slightly
covered (*x1) (Rickenberg et al., 2012). Compared to
the total score – the total number of articles discussing
the topic, the weighted total will better inform the
significance and trends of each unit of analysis.
Based on Table 2, recommender systems catch
the most attention in the domain of audio-visual and
interactive media with the highest intensity (70). The
application of recommender systems in the domain of
heritage and libraries keeps the second place with the
level of high intensity (59). However, the number of
articles discussing this domain (23) is less than those
in the domain of music (28). Only a small portion of
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the reviewed articles deals with recommender
systems in the domains of visual and applied arts (11)
and written and published works (18). The literature
scarcely discusses the application of RSs for live
performance, including performing art, festivals, and
celebrations.
Table 2: Classification of the most relevant articles.
Cultural Domains RS Types
Heritage & libraries
Live performance
Visual &
applied arts
Works
Media
Sound recordings
Collaborative
filtering
Content-based
Hybrid
Albanese et al. (2011a) *** * * ***
Chianese et al. (2013) *** ***
Albanese et al. (2011b) *** ** ** ** ***
Borràs et al. (2014) *** *** *** ***
Bartolini et al. (2013) *** ***
Bartolini et al. (2016) *** * * ***
Kabassi (2013) *** ***
Moreno et al. (2013) *** ** ** ***
Cuomo et al. (2015) *** **
Chianese and Piccialli (2016) *** ***
Garcia et al. (2011) *** * ***
Siu et al. (2013) *** * * * * *
Portugal et al. (2018) * * * * * * ** ** **
Villegas et al. (2018) * * ** ** ** ** **
Champiri et al. (2015) *** * ** *** *** ***
Noguera et al. (2012) *** ** * ***
Park et al. (2012) * * * * * * ** ** *
Umanets et al. (2014) *** * * ***
Yang and Hwang (2013) *** * ***
Ardissono et al. (2012) *** ** ** **
Sassi et al. (2017) *** * ** ** ** ** **
Deng et al. (2018) *** ** ** ***
Sanchez et al. (2012) *** *** *** ***
Zhang et al. (2017) *** ***
Sejal et al. (2016) *** ***
Felício et al. (2016) *** ***
Tkalcic et al. (2012) *** ***
Lin (2014) ** * * ***
Albadvi and Shahbazi (2009) ** ** * * ***
Bedi and Agarwal (2011) *** ***
Bedi and Vashisth (2014) *** ***
Zhou (2010) *** ***
Hariadi and Nurjanah (2017) *** ***
Alharthi et al. (2018) *** * *** ***
Núñez-Valdéz et al. (2012) *** * * *
Nirwan et al. (2016) *** ***
Mathew et al. (2016) *** *** *** ***
A Literature Review of Recommender Systems for the Cultural Sector
719
Table 2: Classification of the most relevant articles (cont.).
Cultural Domains RS Types
Heritage & libraries
Live performance
Visual &
applied arts
Works
Media
Sound recordings
Collaborative filtering
Content-based
Hybrid
Colombo-Mendoza et al. (2015) *** ***
Katarya and Verma (2017) *** *** ***
Li et al. (2018) *** ** ***
Koren et al. (2009) *** * *** *
Ekstrand et al. (2011) *** * *** * *
Kim et al. (2011a) *** *** * *
Carrer-Neto et al. (2012) *** * * ***
Eirinaki et al. (2018) * * ** * *** * *
Beel et al. (2013) ** *
Kim et al. (2011b) * * *** *
van Capelleveen et al. (2019) * ***
Barragáns-Martínez et al. (2010) *** * ** ***
Bobadilla et al. (2013) *** ** ** ** ***
Jannach et al. (2016) *** * ** *
Hu et al. (2019) *** ***
Moreno et al. (2016) *** *** ***
Briguez et al. (2014) *** ***
Zhang and Min (2016) *** * ***
Pereira and Hruschka (2015) *** ** ***
Viktoratos et al. (2018) ** * * ***
Hyung et al. (2014) *** ***
Lee et al. (2017) *** ***
Lu et al. (2015) * * * *** *** ** ** ***
Christensen and Schiaffino (2011) *** *** * ***
Bauer and Nanopoulos (2014) * ** *** * *
Kaminskas and Ricci (2012) *** ** ** ***
Andjelkovic et al. (2019) *** ***
Zheng et al. (2018) *** *** ***
Sánchez-Moreno et al. (2016) *** ***
Deng et al. (2015) *** *** ***
Horsburgh et al. (2015) *** ***
Liu and Chen (2018) *** ***
Total
23 3 11 18 30 28 46 35 55
Weighted total:
59 3 24 36 70 55 102 62 148
Notation:
Highly covered: ***x3; Moderately covered: **x2; Slightly covered: *x1
Types of RSs. The number of articles covering each
unit of analysis, including collaborative filtering (46),
content-based filtering (35), and hybrid (55) points
out interesting facts. Accordingly, the hybrid
approach receives the most attention from scholars
with the highest intensity (148). This finding is also
supported by previous studies (Umanets et al., 2014;
van Capelleveen et al., 2019). Followed by the hybrid
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approach is the collaborative filtering RS with a high
intensity of 102. The content-based RSs catches the
least attention of scholars with the low intensity (62).
5 DISCUSSION
This paper reviewed 69 academic articles to gain a
comprehensive and in-depth insight into the current
status of the applications of recommender systems in
the cultural sector. The number of reviewed articles is
selected from a broad range of scientific journals to
demonstrate a multifaceted spectrum of scholars and
opinions in this research field. Based on the research
objectives, the discussion focuses on the application
of RSs in the cultural domains and relevant aspects
related to data sources, models and algorithms.
5.1 Applications of RSs
Even though the topic of RSs is traditional in the
information system research; however, the
application of RSs is an up-to-date research stream -
especially in the cultural sector.
Audio-visual and Interactive Media, and Sound
Recording Domains. The finding of this paper
indicates that the applications of RSs dominate in the
domain of audio-visual and interactive media,
followed by the sound recording domain. This finding
reaches an agreement with the previous studies
regarding the blossom of many movies and music
RSs (Andjelkovic et al., 2019; Katarya & Verma,
2017). Netflix’s recommender system would be a
salient example as it contributes more than 75% of the
total movie downloads and rentals (Jannach et al.,
2016). The most popular music recommender system
with a significant impact on the music industry is
Last.fm (Horsburgh et al., 2015; Zheng et al., 2018).
The common goal of RSs in the movie and music RSs
is to navigate and discover songs or movies and then
share them with a specific user community (Eirinaki
et al., 2018; Kaminskas & Ricci, 2012). Therefore,
RSs in these two domains emphasize the significance
of social networks (Carrer-Neto et al., 2012).
Accordingly, hybrid RSs integrated with user-
generated tags for social RSs seem to dominate these
domains as they outperform CF and CB systems
(Kim, Alkhaldi, et al., 2011; Pereira & Hruschka,
2015). Merging various techniques, hybrid RSs can
improve user satisfaction and outperform other
systems (Andjelkovic et al., 2019; Christensen &
Schiaffino, 2011).
Heritage and Libraries Domain. In light of the
heritage and libraries domain, the application of RSs
serves two purposes: to recommend a destination for
visits and to suggest a list of activities for visitors to
enjoy such a destination (Garcia et al., 2011). To
facilitate these purposes, RSs in this cultural domain
often apply information technologies - for example, a
mobile platform - to support on-site
recommendations while users at those cultural sites
can know what to do or where to go (Ardissono et al.,
2012; Chianese et al., 2013). Location and activities-
based RSs are mostly developed upon the content-
based approach (Villegas et al., 2018). However, a
user may not satisfy with all recommendations in
which he or she shows an interest (Yang & Hwang,
2013). Therefore, collaborative filtering techniques
are integrated to form a hybrid system. Hybrid RSs
which combine multiple filtering techniques are the
preferred choice in this domain (Umanets et al.,
2014; van Capelleveen et al., 2019).
Live Performance Domain. Although the
application of RSs in the live performance domain
serves the same purposes as the heritage and libraries
domain, very few RSs are developed and
implemented. This can be explained by the occasional
and short-term characteristics of performance,
festivals, and celebrations. In this domain, investment
in RSs may not yield profitable outcomes.
Written and Published Works Domain. RSs in this
domain give prominence to the importance of trust for
reliable recommendations (Bedi & Agarwal, 2011).
Especially through the platform of Web and social
networks, recommendations from users' opinions are
rated by other users to ensure trustworthiness and
avoid scams (Bedi & Vashisth, 2014; Zhou, 2010).
The hybrid approach still dominates in developing
RSs for books, periodicals, and newspapers (Hariadi
& Nurjanah, 2017). However, it is noticeable that a
significant number of book recommendations are
based on the CB approach (Zhou, 2010). This is
rationalized by the fact that the CB can avoid the cold
start’s problem of lacking data on new items from the
CF approach (Alharthi et al., 2018). CB systems do
not rely on user ratings as they make
recommendations based on metadata of items.
Metadata reveals attributes of items related to unique
identifiers, years of publication, description, legal
aspects, enrichment, technicality, contextual or
demographic information (Alharthi et al., 2018).
Structured and standardized metadata will facilitate
the discoverability of cultural contents via RSs
(Andjelkovic et al., 2019; Eirinaki et al., 2018).
A Literature Review of Recommender Systems for the Cultural Sector
721
Visual and Applied Arts Domain. Only a small
proportion of the literature view discusses the
applications of RSs in the domain of visual and
applied arts. The hybrid approach, which is
considered the most optimal can improve up to 90%
of recommendation quality (F. Deng et al., 2018;
Tkalcic et al., 2012). Hybrid RSs take advantage of
data on users' visual perception from the content-
based approach to overcome the problem of
recommendation reliability of collaborative filtering
techniques (Felício et al., 2016). Metadata on users’
visual perception will function as a source of
contextual information (Tkalcic et al., 2012);
therefore, the problem of different degrees of affinity
among users with similar attributes can be handled
(Felício et al., 2016; Sanchez et al., 2012). The
challenges of aesthetics taste dispersion and
undefined visual tastes of content-based techniques
can also be overcome through recommendation
techniques inferred from a user community of the CF
approach (Sanchez et al., 2012).
5.2 Data Sources
Recommender systems are considered as the
significant application of knowledge management to
recommend user’s relevant products, services, or
contents (Park et al., 2012). Main data sources for
recommender systems are internal databases,
websites, social media, and integrated
devices/sensors (Fan, Lau, & Zhao, 2015; Park et al.,
2012). Data sources for RSs have changed along with
the prosperity of the era of information overload.
The first generation of RSs are built from website
data on purchases, users’ demography and preference
(Bobadilla et al., 2013). In the digital age, user data
can be initially cookies and server logs (Chen,
Chiang, & Storey, 2012). Enterprises can obtain
customer clickstream data logs on visit frequency,
viewed items, and visit time to demystify customer
behaviors (Fan et al., 2015; Park et al., 2012). Users’
demography and preference data reveal information
on age, gender, preferences, cultures, lifestyle,
purchasing power, shopping behaviors, customs and
habits of potential and existing customers (Albanese,
d'Acierno, et al., 2011; Dam, Le Dinh, & Menvielle,
2019). Therefore, the first generation of RSs will
allow enterprises to personalize recommendations for
each customer through data from clickstreams,
customer profiles, mobile call records, and
transactions for better customer satisfaction (Kabassi,
2013; Park et al., 2012).
The second generation of recommender systems
is built from social media data such as friends, likes,
followers, followed, tweets, and posts, etc. (Bobadilla
et al., 2013; Ma, Zhou, Liu, Lyu, & King, 2011).
Social intelligence extracted from user-contributed
data on social media can help enterprises in designing
new products, implementing marketing strategies,
and recommend relevant contents for online users
(Abbasi, Chen, & Salem, 2008; Archak, Ghose, &
Ipeirotis, 2011; Lau, Li, & Liao, 2014). The real-time
property of social intelligence along with its
subjectivity in a specific context is significantly
believed to be more trustworthy, updated, and reliable
compared to traditional sources of information
(Abbasi et al., 2008; Lau et al., 2014). Due to these
characteristics, data from social media can improve
the functions of prediction and recommendation of
recommender systems (Bobadilla et al., 2013; Ma et
al., 2011). Mining user-generated content and web
content will allow enterprises not only to develop
suitable products for customer needs but also to
predict a set of relevant products and recommend the
top ones to the right customers (Payne & Frow, 2005;
Rygielski, Wang, & Yen, 2002). Therefore, the
applications of recommender systems have emerged
in various fields, including music, books, shopping,
applications, TV programs, e-learning material, and
web search (Bobadilla et al., 2013; Park et al., 2012).
One of the challenges faced this generation is to
integrate and consolidate the different data sources
(Le Dinh & Nguyen-Ngoc, 2010).
The third generation of recommender systems is
built from data from integrated devices/sensors
(RIFD, real-time health, location-based, or weather
devices, etc.) as the trends of the Internet of Things
(Bobadilla et al., 2013). In particular, location-based
data which can be traced through mobile devices with
GPS, Wi-Fi, GSM, or Bluetooth, vehicles with GPS,
smart cards (bank cards or transportation cards),
floating sensors (devices with radio frequency
identification), check-in from social networks, can
offer real-time property for recommender systems
(Pan et al., 2013; Scellato, Noulas, & Mascolo, 2011).
These integrated devices /sensors, which represent
the Internet of Things, are catching attention in
building recommender systems (Bobadilla et al.,
2013; Ma et al., 2011).
5.3 Data Mining Models and
Algorithms
Different data mining models such as the association,
classification, clustering, and regression are
commonly used for building recommender systems
(Adomavicius & Tuzhilin, 2005). Association models
are widely used in many RSs with various data
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722
mining techniques such as k-Nearest Neighbor,
Bayesian classifiers, association rules, decision trees,
link analysis, neural networks, and linear regression
(Adomavicius & Tuzhilin, 2005; Park et al., 2012).
As one of the most popular techniques, k-Nearest
Neighbor (k-NN) identifies users with similar
behaviors through their preference ratings and filters
top recommendations are most likely to be purchased
(Albadvi & Shahbazi, 2009). Other common data
mining techniques to develop RSs involve association
rules and link analysis (Park et al., 2012). On the other
hand, heuristics, clustering, and association models
are often applied to make recommendations related to
promotional strategies (Park et al., 2012).
Recommender systems will be able to promote
suitable products for target customers, especially for
movies and shopping industries (Payne & Frow,
2005; Rygielski et al., 2002). To sum up, the most
common data mining techniques to build
recommender systems are k-Nearest Neighbor,
association rules, and link analysis (Koren et al.,
2009; Park et al., 2012).
6 CONCLUSIONS
To facilitate the application of recommender systems
for small and medium-sized organizations and
enterprises (SMOs/SMEs) in the cultural domain, this
paper presents an in-depth literature review of 69
academic articles to gain an understanding of the
current status-quo and related aspects on data sources,
data mining models and algorithms. The concept-
centric approach along with the cluster analysis is
applied to categorize all the selected articles to
examine the coherence and relationships among
them. The results of this study have made important
theoretical and practical contributions.
The findings of this paper reveal the status of the
application of recommender systems in different
domains of the cultural sector. Despite the limited
number of articles in this research stream, articles
discussing the applications of RSs in each cultural
domain scatter all over the literature. Hardly any
paper has categorically synthesized and reviewed the
literature of all six cultural domains, including
heritage and libraries, live performance, visual and
applied arts, written and published works, audio-
visual and interactive media, and sound recording.
Therefore, this paper can be considered as the
pioneered literature review, which fulfills a
significant gap in the cultural sector. Another
theoretical contribution of this paper relates to the
application of information systems in the reflection of
recommender systems for cultural SMEs/SMOs.
Considering the fact that there is little research, which
links the two different domains - information systems
and the cultural sector, the proposed classification
framework can be a reference for researchers to
deepen their studies and enrich literature.
In terms of practical contributions, the literature
review will give cultural SMEs/SMOs an idea on
what type of recommender systems would suit their
needs. The paper also foresees the relevant issues
related to the data sources, algorithms, levels of user
engagement, and requirements for IT infrastructure.
The proposed future research directions also infer
enterprises about state-of-the-art RSs. Catching up
with the trend in the development of RSs will help
enterprises better respond to their needs. Therefore,
the paper will serve as a good starting point for
managers who consider adopting a recommender
system. The successful adoption of a recommender
system would enhance the discoverability of cultural
content and products for enterprises in the cultural
sector. Without any doubt that cultural SMEs/SMOs
which can take advantage of recommender systems
can gain a competitive advantage in the fierce
competition of the big data era.
At present, a conceptual framework for context-
aware recommender systems is being developed. This
framework will be experienced with a case study,
which aims at developing a recommender system for
a regional cultural development organization in
Canada to promote the discoverability of the products
and services of its members.
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