Recommender systems have become fundamental building blocks of modern online products and services, and have a substantial impact on user experience. In the past few years, deep learning methods have attracted a lot of research, and are now heavily used in modern real-world recommender systems. Nevertheless, dealing with recommendations in the cold-start setting, e.g., when a user has done limited interactions in the system, is a problem that remains far from solved. Meta-learning techniques, and in particular optimization-based meta-learning, have recently become the most popular approaches in the academic research literature for tackling the cold-start problem in deep learning models for recommender systems. However, current meta-learning approaches are not practical for real-world recommender systems, which have billions of users and items, and strict latency requirements. In this paper we show that it is possible to obtaining similar, or higher, performance on commonly used benchmarks for the cold-start problem without using meta-learning techniques. In more detail, we show that, when tuned correctly, standard and widely adopted deep learning models perform just as well as newer meta-learning models. We further show that an extremely simple modular approach using common representation learning techniques, can perform comparably to meta-learning techniques specifically designed for the cold-start setting while being much more easily deployable in real-world applications.

翻译：推荐系统已成为现代在线产品和服务的基础构建模块，对用户体验具有重大影响。过去几年中，深度学习方法吸引了大量研究关注，并已广泛应用于现代实际推荐系统。然而，在冷启动场景下（例如用户与系统交互有限时）进行推荐，仍是一个远未解决的问题。元学习技术，尤其是基于优化的元学习，近期已成为学术研究文献中应对推荐系统深度学习模型冷启动问题的最主流方法。然而，当前的元学习方法并不适用于拥有数十亿用户和物品且延迟要求严格的现实推荐系统。本文表明，在不使用元学习技术的情况下，同样可以在常用冷启动基准测试上取得相似或更优的性能。具体而言，我们证明，经过恰当调优后，标准且广泛采用的深度学习模型表现与较新的元学习模型不相上下。我们进一步展示，采用常见表示学习技术的极简模块化方法，其性能可与专为冷启动场景设计的元学习技术相媲美，同时在实际应用中更易于部署。