mlsanityData to 14 Oct 2026

Integrating Item Relevance in Training Loss for Sequential Recommender Systems

arXiv 2305.10824 ↗First posted 18 May 2023cs.IR cs.AIcited by 4

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Abstract (arXiv)

Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However, user interactions can be affected by noise stemming from account sharing, inconsistent preferences, or accidental clicks. To address this issue, we (i) propose a new evaluation protocol that takes multiple future items into account and (ii) introduce a novel relevance-aware loss function to train a SRS with multiple future items to make it more robust to noise. Our relevance-aware models obtain an improvement of ~1.2% of NDCG@10 and 0.88% in the traditional evaluation protocol, while in the new evaluation protocol, the improvement is ~1.63% of NDCG@10 and ~1.5% of HR w.r.t the best performing models.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 44 references that are in the map. First posted on arXiv 18 May 2023.

Builds on

19 of 44 references.

References · 19

  1. 2018Self-Attentive Sequential Recommendationcited by 1,184
  2. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 964
  3. 2015Session-based Recommendations with Recurrent Neural Networkscited by 695
  4. 2019Neural Graph Collaborative Filteringcited by 661
  5. 2018Personalized Top-N Sequential Recommendation via Convolutional Sequence Embeddingcited by 476
  6. 2017Neural Attentive Session-based Recommendationcited by 237
  7. 2017Recurrent Neural Networks with Top-k Gains for Session-based Recommendationscited by 165
  8. 2017Deep Learning based Recommender System: A Survey and New Perspectivescited by 136
  9. 2019Sequential Recommender Systems: Challenges, Progress and Prospectscited by 115
  10. 2021Sequential Recommendation with Graph Neural Networkscited by 89
  11. 2020Denoising Implicit Feedback for Recommendationcited by 80
  12. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
7 more
  1. 2018Sequence-Aware Recommender Systemscited by 58
  2. 2017ATRank: An Attention-Based User Behavior Modeling Framework for Recommendationcited by 45
  3. 2017Current Challenges and Visions in Music Recommender Systems Researchcited by 42
  4. 2020A Critical Study on Data Leakage in Recommender System Offline Evaluationcited by 39
  5. 2022Rank List Sensitivity of Recommender Systems to Interaction Perturbationscited by 20
  6. 2022Effective and Efficient Training for Sequential Recommendation using Recency Samplingcited by 13
  7. 2023Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systemscited by 7

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Cited by year

Citing papers per year: 2023: 2; 2024: 2.

4 papers.

  1. 2023Fauno: The Italian Large Language Model that will leave you senza parole!cited by 13
  2. 2024Autoregressive Generation Strategies for Top-K Sequential Recommendationscited by 5
  3. 2023Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbationscited by 2
  4. 2024Eco-Aware Graph Neural Networks for Sustainable Recommendationscited by 1