research-article

Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware Recommendations

Authors:

Giuseppe Spillo,

Marco Polignano,

Marco de Gemmis,

Giovanni SemeraroAuthors Info & Claims

UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Pages 1 - 12

https://doi.org/10.1145/3565472.3592965

Published: 19 June 2023 Publication History

Abstract

In this paper, we present a strategy to provide users with knowledge-aware recommendations based on the combination of graph neural networks and sentence encoders. In particular, our approach relies on the intuition that different data sources (i.e., structured data available in a knowledge graph and unstructured data, such as textual content) provide complementary information and can equally contribute to learn an accurate item representation. Accordingly, we first exploited graph neural networks to encode both collaborative features, such as the interactions between users and items, and structured properties of the items. Next, we used a sentence encoder that relies on transformers to learn a representation based on textual content describing the items. Finally, these embeddings are combined by exploiting a deep neural network where both self-attention and cross-attention mechanisms are used to learn the relationships between the initial embeddings and to further refine the representation. Such a neural network provides as output a prediction of users’ interest in the items, which is used to return a top-k recommendation list. In the experimental evaluation, we carried out an experiment against two datasets, and the results showed that our approach overcame several competitive baselines.

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Petruzzelli AMusto CLaraspata LRinaldi Ide Gemmis MLops PSemeraro G(2024)Instructing and Prompting Large Language Models for Explainable Cross-domain RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688137(298-308)Online publication date: 8-Oct-2024
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Index Terms

Combining Graph Neural Networks and Sentence Encoders for Knowledge-aware Recommendations
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

June 2023

333 pages

ISBN:9781450399326

DOI:10.1145/3565472

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Published: 19 June 2023

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Petruzzelli AMusto CLaraspata LRinaldi Ide Gemmis MLops PSemeraro G(2024)Instructing and Prompting Large Language Models for Explainable Cross-domain RecommendationsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688137(298-308)Online publication date: 8-Oct-2024
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Dibenedetto G(2024)A New Perspective in Health Recommendations: Integration of Human Pose EstimationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688026(1382-1387)Online publication date: 8-Oct-2024
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