Integrating Item Relevance in Training Loss for Sequential Recommender Systems

Abstract

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.

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• Neural Graph Collaborative Filtering
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• Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
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Cited by in corpus (4)

• Investigating the Robustness of Sequential Recommender Systems Against Training Data Perturbations
• Eco-Aware Graph Neural Networks for Sustainable Recommendations
• Autoregressive Generation Strategies for Top-K Sequential Recommendations
• Sheaf4Rec: Sheaf Neural Networks for Graph-based Recommender Systems