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extended-abstract

Integrating Item Relevance in Training Loss for Sequential Recommender Systems

Authors:

Federico Siciliano,

Nicola Tonellotto,

Fabrizio SilvestriAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 1114 - 1119

https://doi.org/10.1145/3604915.3610643

Published: 14 September 2023 Publication History

Abstract

Sequential Recommender Systems (SRSs) are a popular type of recommender system that leverages user history to predict the next item of interest. However, the presence of noise in user interactions, stemming from account sharing, inconsistent preferences, or accidental clicks, can significantly impact the robustness and performance of SRSs, particularly when the entire item set to be predicted is noisy. This situation is more prevalent when only one item is used to train and evaluate the SRSs. To tackle this challenge, we propose a novel approach that addresses the issue of noise in SRSs. First, we propose a sequential multi-relevant future items training objective, leveraging a loss function aware of item relevance, thereby enhancing their robustness against noise in the training data. Additionally, to mitigate the impact of noise at evaluation time, we propose multi-relevant future items evaluation (MRFI-evaluation), aiming to improve overall performance. Our relevance-aware models obtain an improvement of 1.58% of NDCG@10 and 0.96% in terms of HR@10 in the traditional evaluation protocol, the one which utilizes one relevant future item. In the MRFI-evaluation protocol, using multiple future items, the improvement is 2.82% of NDCG@10 and 0.64% of HR@10 w.r.t the best baseline model.

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Cited By

Guarrasi VSiciliano FSilvestri F(2024)RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687106(1265-1269)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687106
Sbandi ASiciliano FSilvestri FSerra ESpezzano F(2024)Mitigating Extreme Cold Start in Graph-based RecSys through Re-rankingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680069(4844-4851)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680069
Betello FSiciliano FMishra PSilvestri F(2024)Investigating the Robustness of Sequential Recommender Systems Against Training Data PerturbationsAdvances in Information Retrieval10.1007/978-3-031-56060-6_14(205-220)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_14

Index Terms

Integrating Item Relevance in Training Loss for Sequential Recommender Systems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information retrieval
    1. Evaluation of retrieval results
      1. Relevance assessment
    2. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 Owner/Author.

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: 14 September 2023

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Extended-abstract
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Refereed limited

Funding Sources

European Union - MUR National Recovery and Resilience Plan
PNRR MUR
European Union - MUR National Recovery and Resilience Plan
PRIN 2020
RC Advanced Grant
ERC Starting Grant
EC H2020RIA

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RecSys '23

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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

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Cited By

Guarrasi VSiciliano FSilvestri F(2024)RobustRecSys @ RecSys2024: Design, Evaluation and Deployment of Robust Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3687106(1265-1269)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3687106
Sbandi ASiciliano FSilvestri FSerra ESpezzano F(2024)Mitigating Extreme Cold Start in Graph-based RecSys through Re-rankingProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3680069(4844-4851)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3680069
Betello FSiciliano FMishra PSilvestri F(2024)Investigating the Robustness of Sequential Recommender Systems Against Training Data PerturbationsAdvances in Information Retrieval10.1007/978-3-031-56060-6_14(205-220)Online publication date: 24-Mar-2024
https://dl.acm.org/doi/10.1007/978-3-031-56060-6_14

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