This paper proposes a sparse factorization approach, KGFlex, that represents each item feature as an embedding. With KGFlex, the user-item interactions are a factorized combination of the item features relevant to the user. An entropy-driven module drives the training considering only the feature involved in the user's decision-making process. Extensive experiments confirm the approach's effectiveness, considering the ranking accuracy, diversity, and induced bias. The public implementation of KGFlex is available at https://split.to/kgflex. © 2022 CEUR-WS. All rights reserved.
Inferring User Decision-Making Processes in Recommender Systems with Knowledge Graphs / Walter Anelli, Vito; Di Noia, Tommaso; Di Sciascio, Eugenio; Ferrara, Antonio; Mancino, ALBERTO CARLO MARIA. - (2022). (Intervento presentato al convegno 30th Italian Symposium on Advanced Database Systems, SEBD 2022 tenutosi a Tirrenia(PI), Italy).
Inferring User Decision-Making Processes in Recommender Systems with Knowledge Graphs
Alberto Carlo Maria Mancino
2022
Abstract
This paper proposes a sparse factorization approach, KGFlex, that represents each item feature as an embedding. With KGFlex, the user-item interactions are a factorized combination of the item features relevant to the user. An entropy-driven module drives the training considering only the feature involved in the user's decision-making process. Extensive experiments confirm the approach's effectiveness, considering the ranking accuracy, diversity, and induced bias. The public implementation of KGFlex is available at https://split.to/kgflex. © 2022 CEUR-WS. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
