Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms. Their graph-based structure paves the way for the adoption of graph-aware learning models such as Graph Neural Networks (GNNs). In this respect, state-of-the-art models achieve good performance and interpretability via user-level combinations of intents leading users to their choices. Unfortunately, such results often come from and end-to-end learnings that considers a combination of the whole set of features contained in the KG without any analysis of the user decisions. In this paper, we introduce KGTORe, a GNN-based model that exploits KG to learn latent representations for the semantic features, and consequently, interpret the user decisions as a personal distillation of the item feature representations. Differently from previous models, KGTORe does not need to process the whole KG at training time but relies on a selection of the most discriminative features for the users, thus resulting in improved performance and personalization. Experimental results on three well-known datasets show that KGTORe achieves remarkable accuracy performance and several ablation studies demonstrate the effectiveness of its components. The implementation of KGTORe is available at: https://github.com/sisinflab/KGTORe.

KGTORe: Tailored Recommendations through Knowledge-aware GNN Models / Mancino, ALBERTO CARLO MARIA; Ferrara, Antonio; Bufi, Salvatore; Malitesta, Daniele; Di Noia, Tommaso; Di Sciascio, Eugenio. - (2023), pp. 576-587. (Intervento presentato al convegno 17th ACM Conference on Recommender Systems, RecSys 2023 tenutosi a Singapore; Singapore) [10.1145/3604915.3608804].

KGTORe: Tailored Recommendations through Knowledge-aware GNN Models

Alberto Carlo Maria Mancino
Primo
;
Antonio Ferrara
;
2023

Abstract

Knowledge graphs (KG) have been proven to be a powerful source of side information to enhance the performance of recommendation algorithms. Their graph-based structure paves the way for the adoption of graph-aware learning models such as Graph Neural Networks (GNNs). In this respect, state-of-the-art models achieve good performance and interpretability via user-level combinations of intents leading users to their choices. Unfortunately, such results often come from and end-to-end learnings that considers a combination of the whole set of features contained in the KG without any analysis of the user decisions. In this paper, we introduce KGTORe, a GNN-based model that exploits KG to learn latent representations for the semantic features, and consequently, interpret the user decisions as a personal distillation of the item feature representations. Differently from previous models, KGTORe does not need to process the whole KG at training time but relies on a selection of the most discriminative features for the users, thus resulting in improved performance and personalization. Experimental results on three well-known datasets show that KGTORe achieves remarkable accuracy performance and several ablation studies demonstrate the effectiveness of its components. The implementation of KGTORe is available at: https://github.com/sisinflab/KGTORe.
2023
17th ACM Conference on Recommender Systems, RecSys 2023
recommendation, knowledge graphs, graph neural networks
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
KGTORe: Tailored Recommendations through Knowledge-aware GNN Models / Mancino, ALBERTO CARLO MARIA; Ferrara, Antonio; Bufi, Salvatore; Malitesta, Daniele; Di Noia, Tommaso; Di Sciascio, Eugenio. - (2023), pp. 576-587. (Intervento presentato al convegno 17th ACM Conference on Recommender Systems, RecSys 2023 tenutosi a Singapore; Singapore) [10.1145/3604915.3608804].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1690628
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