Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions of users. One common issue in real RS is the cold-start problem, where users and items may not contain enough information to produce high-quality recommendations. This work focuses on a complementary problem: recommending new users and items unseen (out-of-vocabulary, or OOV) at training time. This setting is known as the inductive setting and is especially problematic for factorization-based models, which rely on encoding only those users/items seen at training time with fixed parameter vectors. Many existing solutions applied in practice are often naive, such as assigning OOV users/items to random buckets. In this work, we tackle this problem and propose approaches that better leverage available user/item features to improve OOV handling at the embedding table level. We discuss general-purpose plug-and-play approaches that are easily applicable to most RS models and improve inductive performance without negatively impacting transductive model performance. We extensively evaluate 9 OOV embedding methods on 5 models across 4 datasets (spanning different domains). One of these datasets is a proprietary production dataset from a prominent RS employed by a large social platform serving hundreds of millions of daily active users. In our experiments, we find that several proposed methods that exploit feature similarity using LSH consistently outperform alternatives on most model-dataset combinations, with the best method showing a mean improvement of 3.74% over the industry standard baseline in inductive performance. We release our code and hope our work helps practitioners make more informed decisions when handling OOV for their RS and further inspires academic research into improving OOV support in RS.

翻译：推荐系统（RS）因其对数十亿用户日常在线体验的广泛影响，已成为学术界和工业界研究者日益关注的领域。实际推荐系统中的一个常见问题是冷启动问题，即用户和商品可能缺乏足够信息以生成高质量推荐。本文聚焦于一个互补性问题：在训练阶段推荐未见过的（词汇外，简称OOV）新用户和新商品。这一设定被称为归纳式设定，对于依赖固定参数向量仅编码训练时所见用户/商品的分解模型尤为棘手。实践中许多现有解决方案往往较为简单，例如将OOV用户/商品随机分配到哈希桶中。本文针对此问题提出方法，通过更充分地利用用户/商品特征，在嵌入表层面改进OOV处理能力。我们讨论通用即插即用方法，这些方法易于适用于大多数推荐系统模型，可在不损害直推式模型性能的前提下提升归纳式性能。我们基于5个模型、4个数据集（涵盖不同领域）对9种OOV嵌入方法进行了广泛评估。其中一份数据集来自某大型社交平台（服务数亿日活跃用户）采用的主流推荐系统的专有生产数据。实验发现，利用局部敏感哈希（LSH）利用特征相似性的几种提议方法在大多数模型-数据集组合上持续优于其他方法，最佳方法相较于行业标准基线在归纳式性能上平均提升3.74%。我们开源代码，希望工作能协助从业者更明智地处理推荐系统中的OOV问题，并进一步激发学术界对改进推荐系统OOV支持的研究。