May 15, 2024, 4:42 a.m. | Oliver Baumann, Durgesh Nandini, Anderson Rossanez, Mirco Schoenfeld, Julio Cesar dos Reis

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.08465v1 Announce Type: cross
Abstract: Traditional recommendation proposals, including content-based and collaborative filtering, usually focus on similarity between items or users. Existing approaches lack ways of introducing unexpectedness into recommendations, prioritizing globally popular items over exposing users to unforeseen items. This investigation aims to design and evaluate a novel layer on top of recommender systems suited to incorporate relational information and suggest items with a user-defined degree of surprise. We propose a Knowledge Graph (KG) based recommender system by encoding …

abstract arxiv collaborative collaborative filtering cs.ai cs.ir cs.lg cs.mm cs.si design filtering focus graph graph-based investigation knowledge metrics network popular proposals recommendation recommendations type

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