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
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TL;DR: Recommenders are systemsﻅ thatﻴemployﻵ someﻷ knowledge-based�someﻹ knowledge onﻱ someｿknowledge-based ﻵsomeｽ knowledge on ﻷ some，andﻰ personalisedﻳ content onｬ services.
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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.