SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914846828658688 |
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| author | Yang, Xiaodong Chen, Huiyuan Yan, Yuchen Tang, Yuxin Zhao, Yuying Xu, Eric Cai, Yiwei Tong, Hanghang |
| author_facet | Yang, Xiaodong Chen, Huiyuan Yan, Yuchen Tang, Yuxin Zhao, Yuying Xu, Eric Cai, Yiwei Tong, Hanghang |
| contents | The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones. However, BPR often experiences slow convergence and suboptimal local optima, partially because it only considers one negative item for each positive item, neglecting the potential impacts of other unobserved items. To address this issue, the recently proposed Sampled Softmax Cross-Entropy (SSM) compares one positive sample with multiple negative samples, leading to better performance. Our comprehensive experiments confirm that recommender systems consistently benefit from multiple negative samples during training. Furthermore, we introduce a \underline{Sim}plified Sampled Softmax \underline{C}ross-\underline{E}ntropy Loss (SimCE), which simplifies the SSM using its upper bound. Our validation on 12 benchmark datasets, using both MF and LightGCN backbones, shows that SimCE significantly outperforms both BPR and SSM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_16170 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering Yang, Xiaodong Chen, Huiyuan Yan, Yuchen Tang, Yuxin Zhao, Yuying Xu, Eric Cai, Yiwei Tong, Hanghang Information Retrieval Artificial Intelligence The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones. However, BPR often experiences slow convergence and suboptimal local optima, partially because it only considers one negative item for each positive item, neglecting the potential impacts of other unobserved items. To address this issue, the recently proposed Sampled Softmax Cross-Entropy (SSM) compares one positive sample with multiple negative samples, leading to better performance. Our comprehensive experiments confirm that recommender systems consistently benefit from multiple negative samples during training. Furthermore, we introduce a \underline{Sim}plified Sampled Softmax \underline{C}ross-\underline{E}ntropy Loss (SimCE), which simplifies the SSM using its upper bound. Our validation on 12 benchmark datasets, using both MF and LightGCN backbones, shows that SimCE significantly outperforms both BPR and SSM. |
| title | SimCE: Simplifying Cross-Entropy Loss for Collaborative Filtering |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2406.16170 |