=Paper=
{{Paper
|id=Vol-2811/Paper10
|storemode=property
|title=The Role of Recommender Systems in Cultural Heritage
|pdfUrl=https://ceur-ws.org/Vol-2811/Paper10.pdf
|volume=Vol-2811
|authors=George Pavlidis
}}
==The Role of Recommender Systems in Cultural Heritage==
https://ceur-ws.org/Vol-2811/Paper10.pdf
Digital Culture & Audiovisual Challenges: Interdisciplinary Creativity In Arts And Technology
THE ROLE OF RECOMMENDERS
IN CULTURAL HERITAGE
George Pavlidis
Research Director, Athena Research Centre,
gpavlid@ceti.athena-innovation.gr
Abstract
Recommenders are, typically, systems that exploit knowledge regarding preferences of
users on a set of items, in order to create user recommendations for unknown items.
Recommenders are meant to create meaningful recommendations, enhancing the
content personalisation and reducing the information overload. Applications of this type
of technology have already appeared in the domain of cultural heritage, mainly in the
form of museum and tourism recommenders. This paper reviews and explores the role
of recommenders in cultural heritage and briefly discusses the main concepts,
limitations, challenges and future directions.
Keywords – artificial intelligence, cultural heritage, museum guide, recommendation,
recommender
Introduction
The goal of recommenders is to create meaningful recommendations for users regarding
unknown items. The reasons for such systems mainly include the tackling of the
information overload due to the vast amounts of information that overwhelm the users,
and the personalisation of the served content, which, in general, relates to content that
matches a user’s profile, state of mind and information consumption context (i.e.
educational, recreational) (Adomavicius and Tuzhilin, 2005; Aggarwal, 2016b; Asanov
et al., 2011; Melville and Sindhwani, 2011). The relevant technology draws mainly on
cognitive science, approximation theory, information retrieval, forecasting,
management, and consumer modelling (Adomavicius & Tuzhilin, 2005). The most
simplistic, yet highly effective recommender, suggests items based on popularity; this
recommender is considered as the baseline against which any new method should be
compared and win.
Copyright © 2018 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0) DCAC 2018.
Digital Culture & Audiovisual Challenges: Interdisciplinary Creativity In Arts And Technology
Tapestry (Goldberg, Nichols, Oki, & Terry, 1992) and GroupLens (Resnick,
Iacovou, Suchak, Bergstrom, & Riedl, 1994), are considered to be the fathers of modern
recommenders. It was then that a highly persistent approach in this field, collaborative
filtering, was introduced, formulating the problem as an optimisation, seen either as a
minimisation of a cost of an inaccurate prediction, or as a maximisation of a user’s utility
or satisfaction (Good et al., 1999; Iaquinta, de Gemmis, Lops, Semeraro, & Molino,
2010). In the bibliography in this domain three approaches are identified, the content-
based systems, the collaborative filtering systems, and the hybrid systems (Adomavicius
& Tuzhilin, 2005; Aggarwal, 2016b; Anand & Mobasher, 2005; Bobadilla, Ortega,
Hernando, & Gutiérrez, 2013; Jannach, Zanker, Felfernig, & Friedrich, 2011; Kaminskas
& Ricci, 2012; Konstan, 2004; Lü et al., 2012; Ricci, Rokach, Shapira, & Kantor, 2011).
Various evaluation methods applicable to recommenders and an in- depth discussion can
be found in Aggarwal (2016c).
This paper focuses on their recommender system applications in cultural heritage,
where they appeared, mainly, as techniques to enhance museum visits and tourism
applications. The most significant advances are being listed and a brief discussion
concludes on the main concepts, the limitations, the challenges and possible future
directions.
Recommenders in Cultural Heritage
There is a high volume of published works related to recommenders in the cultural
heritage domain. An identification of the most influential and interesting works in this
field leads to a list of around two innovations per year since 1999. This section lists
some of those works in a chronological order to convey the essence of the progress in
this domain.
In 1999 the Hippie guide (Oppermann & Specht, 1999) was developed as an
electronic guide for adaptive exhibition guidance. The innovation included exploiting
awareness of visitor location and user modelling.
In 2002, the Sotto Voce (Aoki et al., 2002) was developed as a PDA audio guide
focused on social aspects of museum visits, by supporting a mediated sharing of audio
content (termed eavesdropping), and providing location-based recommendations.
Rocchi, Stock, Zancanaro, Kruppa, & Krüger (2004) developed a mobile system
focused on cinematic techniques to enhance engagement, using also user localisation.
Chou, Hsieh, Gandon, & Sadeh (2005) developed a collection of PDA applications
that adapted the recommendations to visitor profiles and visitor behaviours, focusing of
context awareness using a number of sensing technologies, based on approaches by
Miller et al. (2004).
The ARCHIE mobile guide (Luyten et al., 2006) focused on social awareness,
influenced by studies like (Falk & Dierking, 2000), in which Wi-Fi-based visitor
localisation was used.
Grieser, Baldwin, & Bird (2007), presented a recommender based on user
modelling and item features, extracted from textual descriptions, using the typical 𝑡f
Digital Culture & Audiovisual Challenges: Interdisciplinary Creativity In Arts And Technology
− 𝑖𝑑𝑓 approach for the text- based similarity estimation and a probabilistic approach
to assess the likelihood of a path.
Basile et al. (2008), under the framework of the CHAT project, developed a
content-based recommender capable of learning user profiles from static and user-
generated content, as a type of extension of the ITem Recommender (Degemmis, Lops,
& Semeraro, 2007).
Luh & Yang (2008) focused on recommendations based on visitor lifestyles based on
collaborative filtering and a set of lifestyle factors proposed by the authors.
Between 2007 and 2010 several versions of a museum recommender were developed
under the framework of project CHIP (Rijksmuseum). Initially, Pechenizkiy & Calders
(2007) developed a content-based personalisation framework. Wang et al. (2008) proposed
the creation of recommendations based on semantically-enriched museums collections
adopting, again, a content-based approach for PDAs and sensor based localisation. A
2009 version appeared in a student research competition, focused on a mobile
implementation for on-site museum visits (Roes, Stash, Wang, & Aroyo, 2009). Van
Hage, Stash, Wang, & Aroyo (2010) presented a more advanced version of the system,
equipped with routing functionalities based on localisation information.
Huang, Liu, Lee, & Huang (2012) developed a personalised guide, focusing on
museum learning settings, based on a rule-based recommender. The interesting in this
work was that it seems to be among the first to present an experimental evaluation
design that targeted user satisfaction factors, as defined in Ong, Day, & Hsu (2009).
Maehara, Yatsugi, Kim, & Ushiama (2012) developed a recommender that relies on
a semantic network on museum exhibits based on item relations and user preferences,
taking into account the limited timeframe of a visit.
Benouaret & Lenne (2015) proposed a combination of semantics (content-based)
and collaborative filtering to create personalised museum tours, on smart mobile devices.
The researchers used relevance, contextual information, time limitations, localisation,
even weather information, to provide accurate context-aware recommendations.
Keller & Viennet (2015) presented a recommender within the AMMICO project
focused on enhanced audio guidance in museum tours, claiming to tackle the challenges
of the cold-start, the data sparsity, and an inherent over-specialisation as expressed in
(Ardissono, Kuflik, & Petrelli, 2012).
Rossi, Barile, Improta, & Russo (2016) developed a collaborative filtering-based
system to increase both individual and group visitor satisfaction, adopting matrix
factorisation, along with localisation aspects.
Tavcar, Antonya, & Butila (2016) designed a hybrid recommender system within
the eHERITAGE project that is based upon strong mash-up approach influences,
combining technologies such as intelligent virtual assistants, Google Street View and
recommenders.
Hashemi & Kamps (2017) developed a hybrid recommender within project meSch,
adopting the free-roaming museum visit model, thus using localisation, online and on-
site user behaviours, and content- and context-awareness.
Cardoso, Rodrigues, Pereira, & Sardo (2017) developed an association rule- based
Digital Culture & Audiovisual Challenges: Interdisciplinary Creativity In Arts And Technology
approach, within project M5SAR, clearly a hybrid method for museum visit
recommendation, capable of supporting multiple visitors and multiple museums and
sites, using the Apriori algorithm (Agrawal, Srikant, et al., 1994) to learn the rules, and
utilised data from the open dataset of MoMA (Robot, 2018).
Kovavisaruch, Sanpechuda, Chinda, Sornlertlamvanich, & Kamolvej (2017)
developed a probabilistic approach for a system capable of evaluating visitor paths in
order to assess the effectiveness of a given museum exhibition organisation. Although the
system naturally supports museum curators and exhibition designers, a very simplistic
approach was described to exploit the model learned for visit recommendations.
Discussion
Most of the works on recommenders in cultural heritage, still conceptualise the museum
as a gallery-like institution with linear narratives, which reveals a trend in the assumed
visitor models or motivations by the involved researchers. This is a conception that needs
to be revised since in the recent years the museums are transforming, mainly due to
sustainability issues, adopting a different role closely related to education, study and
enjoyment.
It is indicative that the “New toolkit for museum and heritage education” by ICOM-
CECA proposed eighteen methods a museum can use to enhance its educational services
(ICOM- CECA. (2017, Oct.). In this report it is evident that the social and participatory
factors are distributed among all the types of experiences, although there are strong
requirements for support from the stakeholder, as these approaches need interesting
storytelling, careful storyline organisation and fascinating narratives, meaningful and
illustrative content and contextual structuring.
Social engagement and visitor participation aspects have already been considered,
but the stakeholder’s role has not been properly defined and included in the loop, by being
described as a type of repository curator. Although a modern view of an institution like
a museum includes heavy investment on storytelling, history and narratives, aesthetics
and education this has new view has not been considered yet.
From a technical point of view, hybrid recommender approaches have proven
their strength in the cultural heritage domain, matching the complexity inherent in
this domain. Since this is a highly dynamic domain with a large variance in tastes and
various biases, online methods that consider context awareness, temporal dynamics and
biased behaviours can be considered as most appropriate. Modern approaches using
methods like reinforcement learning and agent- based techniques have not appeared yet.
In addition, semantic data and linked open data approaches need to be more seriously
included in the technology in the domain, as more and more repositories and collections
move towards international standards for data interoperability.
Location and context awareness can be easily integrated into cultural heritage
recommenders as the Internet of Everything becomes more and more pervasive with
easily applicable solutions, along with always connected and low-cost high-power
ubiquitous computing, even in small-form devices.
The fast-developing intelligent virtual assistants technology is another important
Digital Culture & Audiovisual Challenges: Interdisciplinary Creativity In Arts And Technology
addition to cultural heritage recommenders, and there are already available cultural
applications which incorporate intelligent guides.
Overall, there is room for further development both in the conceptualisation of the
role of cultural institutions and the motivation of the visitors, and in the technologies
that support an intelligent recommender. Impressive new developments are expected to
appear in the near future.
Conclusion
Recommenders are artificial intelligence systems that have already been proven
efficient in tackling information overload and personalisation in various contexts.
Recommenders have appeared in the cultural heritage domain over the past decade to
tackle personalisation in museum visits and cultural tourism applications. This paper
reviewed works focused on cultural heritage applications of recommenders, a rather
complex domain, in which basically hybrid approaches have been the most successful,
although with limitations and assumptions. Challenges and benefits have been identified
and a critical discussion on the reviewed approaches highlighted the foreseen future
developments.
Acknowledgments
The present work was supported by the project “Computational Science and
Technologies: Data, Content and Interaction/Technologies for Content Analysis in
Culture”, MIS code 5002437, co-financed by Greece and European Union in the
framework of the Operational Programme “Competitiveness, Entrepreneurship and
Innovation” 2014-2020.
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