Towards a Collaborative Filtering Framework for Recommendation in Museums
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TL;DR: A general framework that, by using the Matrix Factorization (MF) approach and a graph representation of a museum, addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum.
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About: This article is published in Procedia Computer Science. The article was published on 01 Oct 2016. and is currently open access. The article focuses on the topics: Recommender system & Collaborative filtering.
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Citations
Artworks Sequences Recommendations for Groups in Museums
Silvia Rossi,Francesco Barile,Clemente Galdi,Luca Russo +3 more
- 01 Jan 2016
TL;DR: This work addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum and presents a general framework to address such problems and evaluates a prototype implementation with both an offline analysis and a pilot study in a simulated museum environment.
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Apollo - A Hybrid Recommender for Museums and Cultural Tourism
George Pavlidis
- 01 Sep 2018
TL;DR: Apollo is a novel hybrid recommender for free-roaming or guided museum visits and cultural tourism based on a new conceptualisation of a visit and the adoption of a minimax (or ‘conservative’) approach towards user satisfaction modelling.
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Recommending Multimedia Information in a Virtual Han Chang’an City Roaming System
TL;DR: A virtual roaming system of Han Chang’an City, with both virtual reality (VR) technology and information recommendation technology, designed to recommend hot topic information and personalized information is presented.
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A comparison of two preference elicitation approaches for museum recommendations
TL;DR: In this work, we present and evaluate 2 state‐of-the-art approaches that share the aim not to rely on individual item ratings.
5
Multi-modality Search and Recommendation on Palestinian Cultural Heritage Based on the Holy-Land Ontology and Extrinsic Semantic Resources
TL;DR: In this paper, a precision-oriented multilingual and multi-criteria semantic-based mobile recommender system specifically targeting Palestine's Cultural Heritage (CH), a country with great historical and cultural importance, is presented.
5
References
Matrix Factorization Techniques for Recommender Systems
TL;DR: As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Recommender Systems Handbook
Francesco Ricci,Lior Rokach,Bracha Shapira,Paul B. Kantor +3 more
- 28 Oct 2010
TL;DR: This handbook illustrates how recommender systems can support the user in decision-making, planning and purchasing processes, and works for well known corporations such as Amazon, Google, Microsoft and AT&T.
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Content-based Recommender Systems: State of the Art and Trends
Pasquale Lops,Marco de Gemmis,Giovanni Semeraro +2 more
- 01 Jan 2011
TL;DR: The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.