![]() The collaborative signal in complex high-order connectivities. Is capable of precisely capturing the criteria preference of users as well as Straightforwardly adopting existing GNN-based recommendation methods, we deviseĪ novel criteria preference-aware light graph convolution CPA-LGC method, which Towards designing a GNN-aided MC recommender system. In light of this, we make the first attempt Learning graph representations, it has been still unexplored how to design MC ![]() Surprisingly, although graph neural networks (GNNs) have been widely applied toĭevelop various recommender systems due to GNN's high expressive capability in Information in a wide range of e-commerce areas, is ubiquitous nowadays. Download a PDF of the paper titled Criteria Tell You More than Ratings: Criteria Preference-Aware Light Graph Convolution for Effective Multi-Criteria Recommendation, by Jin-Duk Park and 3 other authors Download PDF Abstract: The multi-criteria (MC) recommender system, which leverages MC rating ![]()
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