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Applying multi-view based metadata in personalized ranking for recommender systems
2015
Proceedings of the 30th Annual ACM Symposium on Applied Computing - SAC '15
In this paper, we propose a multi-view based metadata extraction technique from unstructured textual content in order to be applied in recommendation algorithms based on latent factors. The solution aims at reducing the problem of intense and time-consuming human effort to identify, collect and label descriptions about the items. Our proposal uses a unsupervised learning method to construct topic hierarchies with named entity recognition as privileged information. We evaluate the technique
doi:10.1145/2695664.2695955
dblp:conf/sac/DominguesSBMPR15
fatcat:7zmmx5ujafa75iqnhvxz2uyuuq