In the last 16 years, more than 200 research articles were published about research-paper recommender systems. We reviewed these articles and present some descriptive statistics in this paper, as well as a discussion about the major advancements and shortcomings and an overview of the most common recommendation concepts and approaches. We found that more than half of the recommendation approaches applied content-based filtering (55 %). Collaborative filtering was applied by only 18 % of the reviewed approaches, and graph-based recommendations by 16 %. Other recommendation concepts included stereotyping, item-centric recommendations, and hybrid recommendations. The content-based filtering approaches mainly utilized papers that the users had authored, tagged, browsed, or downloaded. TF-IDF was the most frequently applied weighting scheme. In addition to simple terms, n-grams, topics, and citations were utilized to model users' information needs. Our review revealed some shortcomings of the current research. First, it remains unclear which recommendation concepts and approaches are the most promising. For instance, researchers reported different results on the performance of content-based and collaborative filtering. Sometimes content-based filtering performed better than collaborative filtering and sometimes it performed worse. We identified three potential reasons for the ambiguity of the results. (A) Several evaluations had limitations. They were based on strongly pruned datasets, few participants in user studies, or did not use appropriate baselines. (B) Some authors provided little information about their algorithms, which makes it difficult to re-implement the approaches. Consequently, researchers use different implementations of the same recommendations approaches, which might lead to variations in the results. (C) We speculated that minor variations in datasets, algorithms, or user populations inevitably lead to strong variations in the performance of the approaches. Hence, finding the most promising approaches is a challenge. As a second limitation, we noted that many authors neglected to take into account factors other than accuracy, for example overall user satisfaction. In addition, most approaches (81 %) neglected the user-modeling process and did not infer information automatically but let users provide keywords, text snippets, or a single paper as input. Information on runtime was provided for 10 % of the approaches. Finally, few research papers had an impact on research-paper recommender systems in practice. We also identified a lack of authority and long-term research interest in the field: 73 % of the authors published no more than one paper on research-paper recommender systems, and there was little cooperation among different co-author groups. We concluded that several actions could improve the research landscape: developing a common evaluation framework, agreement on the information to include in research papers, a stronger focus on non-accuracy aspects and user modeling, a platform for researchers to exchange information, and an open-source framework that bundles the available recommendation approaches.

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Research-paper recommender systems: a literature survey

This article presents the methodology that has been successfully used over the past seven years by an interdisciplinary team to create the International Committee for Documentation of the International Council of Museums (CIDOC) CONCEPTUAL REFERENCE MODEL (CRM), a high-level ontology to enable information integration for cultural heritage data and their correlation with library and archive information. The CIDOC CRM is now in the process to become an International Organization for Standardization (ISO) standard. This article justifies in detail the methodology and design by functional requirements and gives examples of its contents. The CIDOC CRM analyzes the common conceptualizations behind data and metadata structures to support data transformation, mediation, and merging. It is argued that such ontologies are property-centric, in contrast to terminological systems, and should be built with different methodologies. It is demonstrated that ontological and epistemological arguments are equally important for an effective design, in particular when dealing with knowledge from the past in any domain. It is assumed that the presented methodology and the upper level of the ontology are applicable in a far wider domain.

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The CIDOC conceptual reference module: an ontological approach to semantic interoperability of metadata

The increasing availability of (digital) cultural heritage artefacts offers great potential for increased access to art content, but also necessitates tools to help users deal with such abundance of information. User-adaptive art recommender systems aim to present their users with art content tailored to their interests. These systems try to adapt to the user based on feedback from the user on which artworks he or she finds interesting. Users need to be able to depend on the system to competently adapt to their feedback and find the artworks that are most interesting to them. This paper investigates the influence of transparency on user trust in and acceptance of content-based recommender systems. A between-subject experiment (N = 60) evaluated interaction with three versions of a content-based art recommender in the cultural heritage domain. This recommender system provides users with artworks that are of interest to them, based on their ratings of other artworks. Version 1 was not transparent, version 2 explained to the user why a recommendation had been made and version 3 showed a rating of how certain the system was that a recommendation would be of interest to the user. Results show that explaining to the user why a recommendation was made increased acceptance of the recommendations. Trust in the system itself was not improved by transparency. Showing how certain the system was of a recommendation did not influence trust and acceptance. A number of guidelines for design of recommender systems in the cultural heritage domain have been derived from the study's results.

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The effects of transparency on trust in and acceptance of a content-based art recommender

RT: brief, extract, scope and contents note n. ~ 1. A concise summary of the key points of a larger work, often used to assist the reader in determining if that work is likely to be of use. – 2. ART · A genre of painting and other arts that originated in the 20th century, characterized by structures and patterns, rather than representational images and forms. ACA ABBR: Academy of Certified Archivists, Association of Canadian Archivists academy file n. ~ A series, often found in Congressional records, which includes applications for admission to a United States service academy, often with letters of recommendation from the member of Congress.

Glossary of Archival And Records Terminology

Galleries, Libraries, Archives and Museums (short: GLAMs) around the globe are beginning to explore the potential of crowdsourcing, i. e. outsourcing specific activities to a community though an open call. In this paper, we propose a typology of these activities, based on an empirical study of a substantial amount of projects initiated by relevant cultural heritage institutions. We use the Digital Content Life Cycle model to study the relation between the different types of crowdsourcing and the core activities of heritage organizations. Finally, we focus on two critical challenges that will define the success of these collaborations between amateurs and professionals: (1) finding sufficient knowledgeable, and loyal users; (2) maintaining a reasonable level of quality. We thus show the path towards a more open, connected and smart cultural heritage: open (the data is open, shared and accessible), connected (the use of linked data allows for interoperable infrastructures, with users and providers getting more and more connected), and smart (the use of knowledge and web technologies allows us to provide interesting data to the right users, in the right context, anytime, anywhere -- both with involved users/consumers and providers). It leads to a future cultural heritage that is open, has intelligent infrastructures and has involved users, consumers and providers.

Crowdsourcing in the cultural heritage domain: opportunities and challenges

This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks).

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Studying Social Tagging and Folksonomy: A Review and Framework

Documentation of art museum collections has been traditionally written by and for art historians. To make art museum collections broadly accessible, and to enable art museums to engage their communities, means of access need to reflect the perspectives of other groups and communities. Social Tagging (the collective assignment of keywords to resources) and its resulting Folksonomy (the assemblage of concepts expressed in such a cooperatively developed system of classification) offer ways for art museums to engage with their communities and to understand what users of online museum collections see as important. Proof of Concept studies at The Metropolitan Museum of Art compared terms assigned by trained cataloguers and untrained cataloguers to existing museum documentation, and explored the potential for social tagging to improve access to museum collections. These preliminary studies, the results of which are reported here, have shown the potential of social tagging and folksonomy to open museum collection...

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Exploring the potential for social tagging and folksonomy in art museums: Proof of concept

Emerging convergence? Thoughts on museums, archives, libraries, and professional training

A Common Model to Support Interoperable Metadata: Progress report on reconciling metadata requirements from the Dublin Core and INDECS/DOI Communities

The research report from the Principal Investigator of the first IMLS funded steve.museum research project.

Jennifer Trant

Papers

Studying Social Tagging and Folksonomy: A Review and Framework

Exploring the potential for social tagging and folksonomy in art museums: Proof of concept

Emerging convergence? Thoughts on museums, archives, libraries, and professional training

A Common Model to Support Interoperable Metadata: Progress report on reconciling metadata requirements from the Dublin Core and INDECS/DOI Communities

Tagging, Folksonomy and Art Museums: Results of steve.museum's research