Integrating Item Relevance in Training Loss for Sequential Recommender Systems
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911842428780544 |
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| author | Bacciu, Andrea Siciliano, Federico Tonellotto, Nicola Silvestri, Fabrizio |
| author_facet | Bacciu, Andrea Siciliano, Federico Tonellotto, Nicola Silvestri, Fabrizio |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_10824 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Integrating Item Relevance in Training Loss for Sequential Recommender Systems Bacciu, Andrea Siciliano, Federico Tonellotto, Nicola Silvestri, Fabrizio Information Retrieval Artificial Intelligence 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. |
| title | Integrating Item Relevance in Training Loss for Sequential Recommender Systems |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2305.10824 |