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Brazilian Conference on Intelligent Systems

BRACIS 2021: Intelligent Systems pp 309–324Cite as

Time-Dependent Item Embeddings for Collaborative Filtering

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13074))

Abstract

Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user’s interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.

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 #2020/09354-3.

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Notes

  1. 1.

    The well-known competition of recommender systems, organized by the streaming company Netflix from 2006 to 2009: https://www.netflixprize.com/rules.html.

  2. 2.

    GroupLens. Available at: www.grouplens.org/datasets/. Access in August 31, 2021.

  3. 3.

    Data Mining Hackathon on BIG DATA (7 GB) Best Buy mobile web site. Available at: www.kaggle.com/c/acm-sf-chapter-hackathon-big. Access in August 31, 2021.

  4. 4.

    Netflix Prize data. Available at: www.kaggle.com/netflix-inc/netflix-prize-data. Access in August 31, 2021.

  5. 5.

    CiaoDVD Movie Ratings. Available at: www.konect.cc/networks/librec-ciaodvd-movie_ratings/. Access in August 31, 2021.

  6. 6.

    Retailrocket recommender system dataset. Available at: www.kaggle.com/retailrocket/ecommerce-dataset. Access in August 31, 2021.

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Authors and Affiliations

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

    Pedro R. Pires, Amanda C. Pascon & Tiago A. Almeida

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  1. Pedro R. Pires
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  2. Amanda C. Pascon
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Correspondence to Pedro R. Pires .

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  1. Universidade Federal de Sergipe, São Cristóvão, Brazil

    André Britto

  2. Universidade de São Paulo, São Paulo, Brazil

    Karina Valdivia Delgado

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Pires, P.R., Pascon, A.C., Almeida, T.A. (2021). Time-Dependent Item Embeddings for Collaborative Filtering. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-91699-2_22

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