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Accountable Knowledge-aware Recommender Systems

Published: 19 June 2023 Publication History

Abstract

Knowledge-aware algorithms represent one of the most innovative research directions in the area of recommender systems. The use of different types of content representation requires new methods to extract descriptive features to adopt in the recommendation process. The literature on knowledge-aware recommender systems is actually rich and constantly evolving in terms of both techniques and software libraries to implement them. This makes also difficult to define reproducible recommendation pipelines, making the accountability of recommender systems a challenge. This tutorial aims to discuss the most recent trends in the area of knowledge-aware recommender systems, including novel representation methods for textual content, and discuss how to implement reproducible pipelines for knowledge-aware recommender systems. We pursue our goals by using a comprehensive Python framework called ClayRS1 to deal with knowledge-aware recommender systems. We would like to provide: (i) common ground for researchers and practitioners interested in the latest knowledge-aware techniques for user modeling and recommender systems; (ii) a practical way for implementing the whole recommendation pipeline, ranging from the content processing for text to the generation of recommendations and the evaluation of their performance.

References

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Alejandro Bellogín and Alan Said. 2021. Improving accountability in recommender systems research through reproducibility. User Model. User Adapt. Interact. 31, 5 (2021), 941–977. https://doi.org/10.1007/s11257-021-09302-x
[2]
Maurizio Ferrari Dacrema, Simone Boglio, Paolo Cremonesi, and Dietmar Jannach. 2021. A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research. ACM Trans. Inf. Syst. 39, 2 (2021), 20:1–20:49.
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Marco de Gemmis, Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2015. Semantics-Aware Content-Based Recommender Systems. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 119–159.
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Joseph A. Konstan and Gediminas Adomavicius. 2013. Toward identification and adoption of best practices in algorithmic recommender systems research. In Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation, RepSys 2013, Hong Kong, China, October 12, 2013, Alejandro Bellogín, Pablo Castells, Alan Said, and Domonkos Tikk (Eds.). ACM, 23–28. https://doi.org/10.1145/2532508.2532513
[5]
Pasquale Lops, Cataldo Musto, Fedelucio Narducci, and Giovanni Semeraro. 2019. Semantics in Adaptive and Personalised Systems - Methods, Tools and Applications. Springer. https://doi.org/10.1007/978-3-030-05618-6
[6]
Cataldo Musto, Marco de Gemmis, Pasquale Lops, Fedelucio Narducci, and Giovanni Semeraro. 2022. Semantics and Content-based Recommendations. In Recommender Systems Handbook, Francesco Ricci, Lior Rokach, and Bracha Shapira (Eds.). Springer, 251–298.
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Marco Polignano, Marco de Gemmis, and Giovanni Semeraro. 2020. Contextualized BERT Sentence Embeddings for Author Profiling: The Cost of Performances. In Computational Science and Its Applications - ICCSA 2020 - 20th International Conference, Cagliari, Italy, July 1-4, 2020, Proceedings, Part IV(Lecture Notes in Computer Science, Vol. 12252), Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Chiara Garau, Ivan Blecic, David Taniar, Bernady O. Apduhan, Ana Maria A. C. Rocha, Eufemia Tarantino, Carmelo Maria Torre, and Yeliz Karaca (Eds.). Springer, 135–149. https://doi.org/10.1007/978-3-030-58811-3_10
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Marco Polignano, Cataldo Musto, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro. 2021. Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations. In RecSys ’21: Fifteenth ACM Conference on Recommender Systems, Amsterdam, The Netherlands, 27 September 2021 - 1 October 2021, Humberto Jesús Corona Pampín, Martha A. Larson, Martijn C. Willemsen, Joseph A. Konstan, Julian J. McAuley, Jean Garcia-Gathright, Bouke Huurnink, and Even Oldridge (Eds.). ACM, 187–198. https://doi.org/10.1145/3460231.3474272

Cited By

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  • (2023)Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasuresProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609492(554-564)Online publication date: 14-Sep-2023

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cover image ACM Conferences
UMAP '23: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization
June 2023
333 pages
ISBN:9781450399326
DOI:10.1145/3565472
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 19 June 2023

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Author Tags

  1. Accountability
  2. Reproducibility
  3. Semantics-aware representations

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  • Extended-abstract
  • Research
  • Refereed limited

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  • This work fulfills the research objectives of the PNRR project FAIR - Future AI Research, spoke 6 - Symbiotic AI, (CUP: H97G22000210007), funded by the Italian Ministry for Universities and Research (MUR).

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UMAP '23
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  • (2023)Reproducibility Analysis of Recommender Systems relying on Visual Features: traps, pitfalls, and countermeasuresProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3609492(554-564)Online publication date: 14-Sep-2023

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