GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913790183866368 |
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| author | Zhang, Sheng Wang, Maolin Wang, Wanyu Gao, Jingtong Zhao, Xiangyu Yang, Yu Wei, Xuetao Liu, Zitao Xu, Tong |
| author_facet | Zhang, Sheng Wang, Maolin Wang, Wanyu Gao, Jingtong Zhao, Xiangyu Yang, Yu Wei, Xuetao Liu, Zitao Xu, Tong |
| contents | Transformer-based models have gained significant traction in sequential recommender systems (SRSs) for their ability to capture user-item interactions effectively. However, these models often suffer from high computational costs and slow inference. Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations. To tackle these challenges, this paper introduces GLINT-RU, a lightweight and efficient SRS leveraging a single-layer dense selective Gated Recurrent Units (GRU) module to accelerate inference. By incorporating a dense selective gate, GLINT-RU adaptively captures temporal dependencies and fine-grained positional information, generating high-quality latent representations. Additionally, a parallel mixing block infuses fine-grained positional features into user-item interactions, enhancing both recommendation quality and efficiency. Extensive experiments on three datasets demonstrate that GLINT-RU achieves superior prediction accuracy and inference speed, outperforming baselines based on RNNs, Transformers, MLPs, and SSMs. These results establish GLINT-RU as a powerful and efficient solution for SRSs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_10244 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems Zhang, Sheng Wang, Maolin Wang, Wanyu Gao, Jingtong Zhao, Xiangyu Yang, Yu Wei, Xuetao Liu, Zitao Xu, Tong Information Retrieval Artificial Intelligence Transformer-based models have gained significant traction in sequential recommender systems (SRSs) for their ability to capture user-item interactions effectively. However, these models often suffer from high computational costs and slow inference. Meanwhile, existing efficient SRS approaches struggle to embed high-quality semantic and positional information into latent representations. To tackle these challenges, this paper introduces GLINT-RU, a lightweight and efficient SRS leveraging a single-layer dense selective Gated Recurrent Units (GRU) module to accelerate inference. By incorporating a dense selective gate, GLINT-RU adaptively captures temporal dependencies and fine-grained positional information, generating high-quality latent representations. Additionally, a parallel mixing block infuses fine-grained positional features into user-item interactions, enhancing both recommendation quality and efficiency. Extensive experiments on three datasets demonstrate that GLINT-RU achieves superior prediction accuracy and inference speed, outperforming baselines based on RNNs, Transformers, MLPs, and SSMs. These results establish GLINT-RU as a powerful and efficient solution for SRSs. |
| title | GLINT-RU: Gated Lightweight Intelligent Recurrent Units for Sequential Recommender Systems |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2406.10244 |