Abstract
In this paper we present a novel user-centered recommendation approach for multimedia art collections. In particular, preferences (usually coded in the shape of items’ metadata), opinions (textual comments to which it is possible to associate a particular sentiment), behavior (in the majority of cases logs of past items’ observations and actions made by users in the environment), and feedbacks (usually expressed in the form of ratings) are considered and integrated together with items’ features and context information within a general and unique recommendation framework that can support an intelligent browsing of any multimedia repository. Preliminary experiments show the utility of the proposed strategy to perform different browsing tasks.
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Notes
- 1.
In the Cultural Heritage domain different harvesting sets of metadata and possibly domain taxonomies or ontologies can be considered.
- 2.
Note that a positive element \(a_{ij}^k\) of \(A^k\) indicates that \(o_i\) was accessed exactly k steps after \(o_j\) at least once.
- 3.
- 4.
Virtual rooms with paintings are related to specific historical periods.
- 5.
The suggested paths represent the most easy way for the player to find and access the paintings of interest.
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Amato, F., Moscato, V., Picariello, A., Sperlí, G. (2018). A Recommender System for Multimedia Art Collections. In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_21
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