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.

/pdf/the-effects-of-transparency-on-trust-in-and-acceptance-of-a-t5rtj3cgc6.pdf

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

Mobile phones are becoming a primary platform for information access and when coupled with recommender systems technologies they can become key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems providing personalized and more focussed content, hence limiting the negative effects of information overload. In this paper we review the major issues and opportunities that the mobile scenario opens to the application of recommender systems especially in the area of travel and tourism. We overview major techniques that have been proposed in the last years and we illustrate the supported functions. We also illustrate specific computational models that have been proposed for mobile recommender systems and we close the paper by presenting some possible future developments and extension in this area.

/pdf/mobile-recommender-systems-68qt1vm561.pdf

Mobile recommender systems.

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed.

/pdf/personalization-in-cultural-heritage-the-road-travelled-and-35wrqofbvf.pdf

Personalization in cultural heritage: the road travelled and the one ahead

In order to adapt functionality to their individual users, systems need information about these users The Social Web provides opportunities to gather user data from outside the system itself Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start

/pdf/cross-system-user-modeling-and-personalization-on-the-social-46ni0c1gt0.pdf

Cross-system user modeling and personalization on the Social Web

A hypermedia application offers its users much freedom to navigate through a large hyperspace. Adaptive Hypermedia (AH) offers personalized content, presentation and navigation support. Many Adaptive Hypermedia Systems (AHS) are tightly integrated with one specific application and/or use a limited number of techniques and methods. This makes it difficult to capture all of them in one generic model. In this paper we examine adaptation questions stated in the very beginning of the adaptive hypermedia era and elaborate on their recent interpretations. We will reconsider design issues for application independent generic adaptive hypermedia systems, review open questions of system extensibility introduced in adjacent research fields and try to come up with an up-to-date taxonomy of adaptation techniques and an extensive set of requirements for a new adaptive system reference model or architecture, to be developed in the future.1

AH 12 years later : a comprehensive survey of adaptive hypermedia methods and techniques (Extended abstract)

https://www.cs.vu.nl/~guus/papers/Wang08a.pdf

Recommendations based on semantically enriched museum collections

This paper describes a real-time routing system that implements a mobile museum tour guide for providing personalized tours tailored to the user position inside the museum and interests. The core of this tour guide originates from the CHIP (Cultural Heritage Information Personalization) Web-based tools set for personalized access to the Rijksmuseum Amsterdam collection. In a number of previous papers we presented these tools for interactive discovery of user's interests, semantic recommendations of artworks and art-related topics, and the (semi-)automatic generation of personalized museum tours. Typically, a museum visitor could wander around the museum and get attracted by artworks outside of the current tour he is following. To support a dynamic adaptation of the tour to the current user position and changing interests, we have extended the existing CHIP mobile tour guide with a routing mechanism based on the SWI-Prolog Space package. The package uses (1) the CHIP user profile containing user's preferences and current location; (2) the semantically enriched Rijksmuseum collection and (3) the coordinates of the artworks and rooms in the museum. This is a joint work between the Dutch nationally funded CHIP and Poseidon projects and the prototype demonstrator can be found at http://www.chip-project.org/spacechip.

/pdf/finding-your-way-through-the-rijksmuseum-with-an-adaptive-3ke4i7avjm.pdf

Finding your way through the rijksmuseum with an adaptive mobile museum guide

Web 2.0 — the perceived second generation of the World Wide Web that aims to improve collaboration, sharing of information and interoperability — enables increasing access to digital collections of museums. The expectation is that more and more people will spend time preparing their visit before actually visiting the museum and look for related information reflecting on what they have seen or missed after visiting the museum. It can also be expected that museum curators want to enhance visitors' museum experiences in the more personalized, intensive and engaging way promised by an improved Web. In other words, to keep their visitors, they should adopt an immersive museum environment that combines the museum Web site (online) with the physical museum space (on-site). In this context, the CHIP (Cultural Heritage Information Presentation) project offers tools to the users to be their own curators, e.g. browsing the online collections, planning a personalized museum tour suiting their art interests, g...

/pdf/cultivating-personalized-museum-tours-online-and-on-site-1v7o1m76om.pdf

Cultivating Personalized Museum Tours Online and On-Site

The main objective of the CHIP project is to demonstrate how Semantic Web technologies can be deployed to provide personalized access to digital museum collections. We illustrate our approach with the digital database ARIA of the Rijksmuseum Amsterdam. For the semantic enrichment of the Rijksmuseum ARIA database we collaborated with the CATCH STITCH project to produce mappings to Iconclass, and with the MultimediaN E-culture project to produce the RDF/OWL of the ARIA and Adlib databases. The main focus of CHIP is on exploring the potential of applying adaptation techniques to provide personalized experience for the museum visitors both on the Web site and in the museum.

/pdf/chip-demonstrator-semantics-driven-recommendations-and-3prh5dfq45.pdf

CHIP demonstrator: semantics-driven recommendations and museum tour generation

In this paper we present an approach for personalized access to museum collections. We use a RDF/OWL specification of the Rijksmuseum Amsterdam collections as a driver for an interactive dialog. The user gives his/her judgment on the artefacts, indicating likes or dislikes. The elicited user model is further used for generating recommendations of artefacts and topics. In this way we support exploration and discovery of information in museum collections. A user study provided insights in characteristics of our target user group, and showed how novice and expert users employ their background knowledge and implicit interest in order to elicit their art preference in the museum collections.

/pdf/interactive-user-modeling-for-personalized-access-to-museum-24qdklqn0e.pdf

Yiwen Wang

Papers

Recommendations based on semantically enriched museum collections

Finding your way through the rijksmuseum with an adaptive mobile museum guide

Cultivating Personalized Museum Tours Online and On-Site

CHIP demonstrator: semantics-driven recommendations and museum tour generation

Interactive User Modeling for Personalized Access to Museum Collections: The Rijksmuseum Case Study