Neural network approaches in recommender systems have shown remarkable success by representing a large set of items as a learnable vector embedding table. However, infrequent items may suffer from inadequate training opportunities, making it difficult to learn meaningful representations. We examine that in attribute and context-aware settings, the poorly learned embeddings of infrequent items impair the recommendation accuracy. To address such an issue, we propose a proxy-based item representation that allows each item to be expressed as a weighted sum of learnable proxy embeddings. Here, the proxy weight is determined by the attributes and context of each item and may incorporate bias terms in case of frequent items to further reflect collaborative signals. The proxy-based method calculates the item representations compositionally, ensuring each representation resides inside a well-trained simplex and, thus, acquires guaranteed quality. Additionally, that the proxy embeddings are shared across all items allows the infrequent items to borrow training signals of frequent items in a unified model structure and end-to-end manner. Our proposed method is a plug-and-play model that can replace the item encoding layer of any neural network-based recommendation model, while consistently improving the recommendation performance with much smaller parameter usage. Experiments conducted on real-world recommendation benchmark datasets demonstrate that our proposed model outperforms state-of-the-art models in terms of recommendation accuracy by up to 17% while using only 10% of the parameters.

翻译：在推荐系统中，神经网络方法通过将大量物品表示为可学习的向量嵌入表而取得了显著成功。然而，低频物品可能因训练机会不足而难以学习到有意义的表示。我们发现在属性和上下文感知的场景中，学习欠佳的低频物品嵌入会损害推荐准确性。为解决这一问题，我们提出了一种基于代理的物品表示方法，允许每个物品被表示为可学习代理嵌入的加权和。其中，代理权重由每个物品的属性和上下文决定，并可针对高频物品引入偏置项以进一步反映协同信号。基于代理的方法通过组合方式计算物品表示，确保每个表示位于训练良好的单纯形内，从而获得质量保证。此外，由于代理嵌入在所有物品间共享，低频物品能够以统一模型结构和端到端方式借用高频物品的训练信号。我们提出的方法是一种即插即用模型，可替换任何基于神经网络的推荐模型的物品编码层，同时以更少的参数用量持续提升推荐性能。在真实推荐基准数据集上进行的实验表明，我们提出的模型在推荐准确性上比现有最优模型提升高达17%，而参数使用量仅为后者的10%。