Recommendations based on semantically enriched museum collections

Abstract

a b s t r a c tThis article presents the CHIP demonstrator 1 for providing personalized access to digital museum collections. It consists of three main components: Art Recommender, Tour Wizard, and Mobile Tour Guide. Based on the semantically enriched Rijksmuseum Amsterdam 2 collection, we show how Semantic Web technologies can be deployed to (partially) solve three important challenges for recommender systems applied in an open Web context: (1) to deal with the complexity of various types of relationships for recommendation inferencing, where we take a content-based approach to recommend both artworks and art-history topics; (2) to cope with the typical user modeling problems, such as cold-start for first-time users, sparsity in terms of user ratings, and the efficiency of user feedback collection; and (3) to support the presentation of recommendations by combining different views like a historical timeline, museum map and faceted browser. Following a user-centered design cycle, we have performed two evaluations with users to test the effectiveness of the recommendation strategy and to compare the different ways for building an optimal user profile for efficient recommendations. The CHIP demonstrator received the Semantic Web Challenge Award (third prize) in 2007, Busan, Korea.

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

“…At about the same time, CHIP (Wang et al, 2008) experimented with semantic web technologies to enrich the presentation of the Rijskmuseum collection with information retrieved from public ontologies.…”

CultureSampo: A National Publication System of Cultural Heritage on the Semantic Web 2.0

“…The vision and implementation of CULTURESAMPO goes beyond current semantic web portals for cultural heritage [1], such as MuseumFinland [11], MultimediaN 2006 [12], and CHIP Demonstrator 2007 [13]: The CULTURESAMPO system 1) is highly crossdomain with lots of content types and metadata schemas (usually only one schema such as Dublin Core or VRA is used), 2) it makes use of sophisticated semantic annotation models including events and processes, 3) it uses new kind of semantic search and recommendation techniques, 4) it has exceptionally versatile selection of semantic visualizations available (different map views, timelines, graphs, process visualization, semantic video viewing), 5) it is based on a large nation wide collaboratively maintained infrastructure of ontologies and ontology services, 6) it includes a model of and tools for collaborative semantic content creation, and 7) the services are available for machines, too.…”

Learning the emergent knowledge from annotated blog postings

“…The system inferences the preferences of users from the users' postings and connections among locations, users and tags. The similar efforts of processing and finding collective knowledge from social web content with semantic web technologies can be found in [25] (a recommendation system) and [23] (a tutor system). However, such systems utilize the findings only to solve the pre-defined problems.…”