用共享网络结构减少参数量，提升推荐系统效率与泛化能力
Parameter-Efficient Single Collaborative Branch for Recommendation
- 用户与物品模型共用一个神经网络分支，实现参数共享
- 参数量减少但准确率不降，且在多场景下表现更优
- 适合追求高效部署的工业级推荐系统应用
推荐系统通常依赖用户和物品在联合嵌入空间中的表示，通过相似度度量计算相关性分数。现代推荐系统中，获取用户和物品表示的模块由两个独立的神经网络构成。在多模态表征学习中，权重共享已被证明能有效缩小同一物品不同模态间的距离。受此启发，本文提出一种新型推荐框架——单协同分支推荐（CoBraR），其通过在用户与物品神经网络模块间引入权重共享，共同构建共享嵌入空间中的潜在表示。我们在电商和电影推荐数据集上进行了定量实验，结果表明：在减少参数量的同时，该方法在超越准确率的指标上表现更优，且未牺牲准确性，具备在真实场景中应用和扩展的潜力。
原文摘要 · Abstract (English)
Recommender Systems (RS) often rely on representations of users and items in a joint embedding space and on a similarity metric to compute relevance scores. In modern RS, the modules to obtain user and item representations consist of two distinct and separate neural networks (NN). In multimodal representation learning, weight sharing has been proven effective in reducing the distance between multiple modalities of a same item. Inspired by these approaches, we propose a novel RS that leverages weight sharing between the user and item NN modules used to obtain the latent representations in the shared embedding space. The proposed framework consists of a single Collaborative Branch for Recommendation (CoBraR). We evaluate CoBraR by means of quantitative experiments on e-commerce and movie recommendation. Our experiments show that by reducing the number of parameters and improving beyond-accuracy aspects without compromising accuracy, CoBraR has the potential to be applied and extended for real-world scenarios.
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