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Interact2Vec: Neural Item and User Embedding for Collaborative Filtering

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Intelligent Systems (BRACIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13654 ))

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Abstract

Recommender systems gained great popularity in the last decade. However, despite the significant advances, they still have open problems, such as high data dimensionality and sparseness. Among several alternatives proposed to address these problems, the state-of-the-art solutions aim to represent items and users as dense vectors in a reduced dimensionality space. In this context, one of the most contemporary techniques is neural embeddings-based models, i.e., distributed vector representations generated through artificial neural networks. Many of the latest advances in this area have shown promising results compared to established approaches. However, most existing proposals demand complex neural architectures or content data, often unavailable. This paper presents the Interact2Vec, a new neural network-based model for concomitantly generating a distributed representation of users and items. It has the main advantage of being computationally efficient and only requires implicit user feedback. The results indicate a high performance comparable to other neural embeddings models that demand more significant computational power.

This study was financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) Finance Code 88882.426978/2019-01, and Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) grant #2021/14591-7.

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Authors and Affiliations

  1. Department of Computer Science, Federal University of São Carlos, Sorocaba, Brazil

    Pedro R. Pires & Tiago A. Almeida

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  1. Pedro R. Pires
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  2. Tiago A. Almeida
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Correspondence to Pedro R. Pires .

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Editors and Affiliations

  1. Federal University of Rio Grande do Norte, Natal, Brazil

    João Carlos Xavier-Junior

  2. Federal University of Bahia, Salvador, Brazil

    Ricardo Araújo Rios

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Pires, P.R., Almeida, T.A. (2022). Interact2Vec: Neural Item and User Embedding for Collaborative Filtering. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13654 . Springer, Cham. https://doi.org/10.1007/978-3-031-21689-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-21689-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21688-6

  • Online ISBN: 978-3-031-21689-3

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