The cold-start problem is quite challenging for existing recommendation models. Specifically, for the new items with only a few interactions, their ID embeddings are trained inadequately, leading to poor recommendation performance. Some recent studies introduce meta learning to solve the cold-start problem by generating meta embeddings for new items as their initial ID embeddings. However, we argue that the capability of these methods is limited, because they mainly utilize item attribute features which only contain little information, but ignore the useful collaborative information contained in the ID embeddings of users and old items. To tackle this issue, we propose CoMeta to enhance the meta embeddings with the collaborative information. CoMeta consists of two submodules: B-EG and S-EG. Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it. The final meta embedding is obtained by adding up the base embedding and the shift embedding. We conduct extensive experiments on two public datasets. The experimental results demonstrate both the effectiveness and the compatibility of CoMeta.

翻译：摘要：冷启动问题对现有推荐模型极具挑战性。具体而言，对于仅有少量交互记录的新项目，其ID嵌入训练不充分，导致推荐性能欠佳。近期研究引入元学习，通过为新项目生成元嵌入作为初始ID嵌入来解决冷启动问题。然而，我们认为这些方法的能力有限，因为它们主要利用仅包含少量信息的项目属性特征，却忽视了用户和旧项目ID嵌入中包含的有用协同信息。为解决此问题，我们提出CoMeta以协同信息增强元嵌入。CoMeta包含两个子模块：B-EG和S-EG。具体而言，对于新项目：B-EG计算旧项目ID嵌入基于相似度的加权和作为其基础嵌入；S-EG不仅利用新项目的属性特征，还结合与其交互过的用户的平均ID嵌入来生成偏移嵌入。最终元嵌入通过将基础嵌入与偏移嵌入相加获得。我们在两个公开数据集上进行了广泛实验，实验结果证明了CoMeta的有效性与兼容性。