Alessio De Angelis
Fabio Gasparetti
Giuseppe Sansonetti
ABSTRACT
This article describes a recommender system (RS) in the cultural heritage area, which takes into account the activities on social media performed by the target user and her friends. For this purpose, the system exploits linked open data (LOD) as well. More specifically, the proposed RS (i) extracts information from social networks (e.g., Facebook) by analyzing content generated by users and those included in their social networks; (ii) performs disambiguation tasks through LOD tools; (iii) profiles the user as a social graph; (iv) provides the actual user with personalized suggestions of artistic and cultural resources by integrating collaborative filtering algorithms with semantic technologies for leveraging LOD sources such as DBpedia and Europeana.
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Index Terms
- A Social Cultural Recommender based on Linked Open Data
Recommendations
Cross-Domain Recommendation for Enhancing Cultural Heritage Experience
UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and PersonalizationIn this paper, we describe our research activities for integrating the recommendation process of nearby points of artistic and cultural interest (POIs) with related multimedia content. The recommendation engine exploits the potential offered by linked ...
Using Social Media for Personalizing the Cultural Heritage Experience
UMAP '21: Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and PersonalizationThis article presents a personalized recommendation approach of textual and multimedia resources related to artistic and cultural points of interest (POIs). This approach exploits linked open data to retrieve content related to POIs and social media to ...
Enhancing cultural recommendations through social and linked open data
In this article, we describe a hybrid recommender system (RS) in the artistic and cultural heritage area, which takes into account the activities on social media performed by the target user and her friends, and takes advantage of linked open data (LOD) ...
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