short-paper

Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules

Authors:

Giuseppe Spillo,

Marco De Gemmis,

Giovanni SemeraroAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 616 - 621

https://doi.org/10.1145/3523227.3551484

Published: 13 September 2022 Publication History

Abstract

In this paper, we present a knowledge-aware recommendation framework based on neuro-symbolic graph embeddings that encode first-order logical (FOL) rules. In particular, our workflow starts from a knowledge graph (KG) encoding user preferences (based on explicit ratings [13]) and item properties. Next, knowledge-aware recommendation are obtained through the combination of three modules: (i) a rule learner, that extracts FOL rules from the KG; (ii) a graph embedding module, that learns the embeddings of users and items based on the triples of the KG and the FOL rules previously extracted; (iii) a recommendation module that uses the embeddings to feed a deep learning architecture. In the experimental session, we evaluate the effectiveness of our strategy on two datasets and the results show that the combination of KG embeddings and FOL rules led to an improvement in the accuracy and in the novelty of the recommendations.

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Cited By

Zhang XShi SLi YMa WSun PZhang M(2024)Feature-Enhanced Neural Collaborative Reasoning for Explainable RecommendationACM Transactions on Information Systems10.1145/369038143:1(1-33)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690381
Tiribelli SCalvaresi D(2024)Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical AnalysisScience and Engineering Ethics10.1007/s11948-024-00479-z30:3Online publication date: 27-May-2024
https://doi.org/10.1007/s11948-024-00479-z
Bufi SMancino AFerrara AMalitesta DDi Noia TDi Sciascio E(2024)KGUF: Simple Knowledge-Aware Graph-Based Recommender with User-Based Semantic Features FilteringAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_4(41-59)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_4
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Index Terms

Knowledge-aware Recommendations Based on Neuro-Symbolic Graph Embeddings and First-Order Logical Rules
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 ACM.

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Published: 13 September 2022

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Zhang XShi SLi YMa WSun PZhang M(2024)Feature-Enhanced Neural Collaborative Reasoning for Explainable RecommendationACM Transactions on Information Systems10.1145/369038143:1(1-33)Online publication date: 28-Aug-2024
https://dl.acm.org/doi/10.1145/3690381
Tiribelli SCalvaresi D(2024)Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical AnalysisScience and Engineering Ethics10.1007/s11948-024-00479-z30:3Online publication date: 27-May-2024
https://doi.org/10.1007/s11948-024-00479-z
Bufi SMancino AFerrara AMalitesta DDi Noia TDi Sciascio E(2024)KGUF: Simple Knowledge-Aware Graph-Based Recommender with User-Based Semantic Features FilteringAdvances on Graph-Based Approaches in Information Retrieval10.1007/978-3-031-71382-8_4(41-59)Online publication date: 10-Oct-2024
https://doi.org/10.1007/978-3-031-71382-8_4
Carraro T(2023)Overcoming Recommendation Limitations with Neuro-Symbolic IntegrationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608876(1325-1331)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608876
Spillo G(2023)Knowledge-Aware Recommender Systems based on Multi-Modal Information SourcesProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608866(1312-1317)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608866
Mancino AFerrara ABufi SMalitesta DDi Noia TDi Sciascio E(2023)KGTORe: Tailored Recommendations through Knowledge-aware GNN ModelsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608804(576-587)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608804
Li PLi XSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)OPORP: One Permutation + One Random ProjectionProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599457(1303-1315)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599457
Spillo G(2023)Combining Heterogeneous Embeddings for Knowledge-Aware Recommendation ModelsProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3595615(269-273)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3595615

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