GInRec: A Gated Architecture for Inductive Recommendation using Knowledge Graphs

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Abstract

We have witnessed increasing interest in exploiting KGs to integrate contextual knowledge in recommender systems in addition to user-item interactions, e.g., ratings. Yet, most methods are transductive, i.e., they represent instances seen during training as low-dimensionality vectors but cannot do so for unseen instances. Hence, they require heavy retraining every time new items or users are added. Conversely, inductive methods promise to solve these issues. KGs enhance inductive recommendation by offering information on item-entity relationships, whereas existing inductive methods rely purely on interactions, which makes recommendations for users with few interactions sub-optimal and even impossible for new items. In this work, we investigate the actual ability of inductive methods exploiting both the structure and the data represented by KGs. Hence, we propose GInRec, a state-of-the-art method that uses a graph neural network with relation-specific gates and a KG to provide better recommendations for new users and items than related inductive methods. As a result, we re-evaluate state-of-the-art methods, identify better evaluation protocols, highlight unwarranted conclusions from previous proposals, and showcase a novel, stronger architecture for this task. The source code is available at: https://github.com/theisjendal/kars2023-recommendation-framework.
Original languageEnglish
Title of host publicationKaRS 2023 Knowledge-aware and Conversational Recommender Systems 2023 : Proceedings of the Fifth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 17th ACM Conference on Recommender Systems (RecSys 2023)
EditorsVito Walter Anelli, Pierpaolo Basile, Gerard De Melo, Francesco Maria Donini, Antonio Ferrara, Cataldo Musto, Fedelucio Narducci, Azzurra Ragone, Markus Zanker
Number of pages10
PublisherCEUR Workshop Proceedings
Publication date23 Nov 2023
Pages80-89
Publication statusPublished - 23 Nov 2023
EventKnowledge-aware and Conversational Recommender Systems Workshop 2023 - , Singapore
Duration: 18 Sept 202322 Sept 2023

Conference

ConferenceKnowledge-aware and Conversational Recommender Systems Workshop 2023
Country/TerritorySingapore
Period18/09/202322/09/2023
SeriesCEUR Workshop Proceedings
Volume3560
ISSN1613-0073

Keywords

  • Recommendation System
  • New users
  • Graph neural network
  • Knowledge Graph
  • Relational Gating Mechanism

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