entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation

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DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “nt FactorModel[19],NeuralTensorNetwork[39]andSemanticMatching Energy [4]. Recently, the knowledge graph has also been used in many applications, such as movie recommendation[52], top-N recommendation [35], machine reading[51], text classification[46] 20 50 100 200 0.60 0.62 0.64 0.66 0.68 k AUC d=20”
Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (2019, methodology): “ore features for the recommendation. There are two types of works that utilize knowledge graphs to improve recommendation: meta-path based methods [32, 43, 48] and embedding learning-based algorithms [24, 31, 44]. However, meta-pathbasedmethodsrequiremanualeffortsanddomainknowledge to define pattern”
A Survey on Knowledge Graph-Based Recommender Systems (2020, methodology): “After obtaining the embeddings of entities in the graph, the user’s preference can be calculated with Equation 1, or by further KDD 2016 entity2rec [66] RecSys 2017 ECFKG [67] Algorithms 2018 SHINE [68] WSDM 2018 DKN [48] WWW 2018 KSR [44] SIGIR 2018 CFKG [13] SIGIR 2018 KTGAN [69] ICDM 2018 KTUP…”