A Wiki-based Environment for Constraint-based Recommender Systems Applied in the E-Government Domain.
UMAP Workshops(2015)
摘要
Constraint-based recommenders support customers in identifying relevant items from complex item assortments. In this paper we present WeeVis, a constraint-based environment that can be applied in different scenarios in the e-government domain. WeeVis supports collaborative knowledge acquisition for recommender applications in a MediaWiki-based context. This paper shows how Wiki pages can be extended with recommender applications and how the environment uses intelligent mechanisms to support users in identifying the optimal solutions to their needs. An evaluation shows a performance overview with different knowledge bases.
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