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Personalised Visual Art Recommendation by Learning Latent Semantic Representations
[article]
2020
arXiv
pre-print
In Recommender systems, data representation techniques play a great role as they have the power to entangle, hide and reveal explanatory factors embedded within datasets. Hence, they influence the quality of recommendations. Specifically, in Visual Art (VA) recommendations the complexity of the concepts embodied within paintings, makes the task of capturing semantics by machines far from trivial. In VA recommendation, prominent works commonly use manually curated metadata to drive
arXiv:2008.02687v1
fatcat:7c3k4bsl2vhkjhiydg3qcqfpaq