The cold-start scenario is a critical problem for recommendation systems, especially in dynamically changing domains such as online news services. In this research, we aim at addressing the cold-start situation by adapting an unsupervised neural User2Vec method to represent new users and articles in a multidimensional space. Toward this goal, we propose an extension of the Doc2Vec model that is capable of representing users with unknown history by building embeddings of their metadata labels along with item representations. We evaluate our proposed approach with respect to different parameter configurations on three real-world recommendation datasets with different characteristics. Our results show that this approach may be applied as an efficient alternative to the factorization machine-based method when the user and item metadata are used and hence can be applied in the cold-start scenario for both new users and new items. Additionally, as our solution represents the user and item labels in the same vector space, we can analyze the spatial relations among these labels to reveal latent interest features of the audience groups as well as possible data biases and disparities.

中文翻译：

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用于解决推荐系统中用户和项目冷启动问题的 Meta-User2Vec 模型

冷启动场景是推荐系统的一个关键问题，尤其是在动态变化的领域，例如在线新闻服务。在这项研究中，我们的目标是通过采用无监督神经 User2Vec 方法在多维空间中表示新用户和文章来解决冷启动情况。为了实现这一目标，我们提出了 Doc2Vec 模型的扩展，该模型能够通过构建元数据标签和项目表示的嵌入来表示具有未知历史的用户。我们针对具有不同特征的三个真实世界推荐数据集的不同参数配置来评估我们提出的方法。我们的结果表明，当使用用户和项目元数据时，这种方法可以作为基于分解机的方法的有效替代方法，因此可以应用于新用户和新项目的冷启动场景。此外，由于我们的解决方案在同一向量空间中表示用户和项目标签，我们可以分析这些标签之间的空间关系，以揭示受众群体的潜在兴趣特征以及可能的数据偏差和差异。