论文标题
实践中列入推荐系统：诱导学习和增量更新
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
论文作者
论文摘要
随着信息系统变得更大，推荐系统是对机器学习研究和行业越来越感兴趣的话题。即使改进模型设计的进展在研究方面迅速，我们认为许多进步由于两个有限的假设而无法转化为实践。首先，大多数方法都集中在无法处理看不见的用户或项目的转导学习设置上，其次，为静态设置开发了许多现有方法，这些方法是无法在可用时结合新数据的静态设置。我们认为，这些在实时发生新的用户互动的现实平台上，这些假设在很大程度上是不切实际的假设。在本调查文件中，我们将概念正式化，并从过去六年开始进行推荐系统的上下文化。然后，我们讨论为什么未来的工作以及如何朝着归纳学习和增量更新迈向推荐模型设计和评估。此外，我们为未来的研究提出了最佳实践和基本开放挑战。
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving model design has been rapid in research, we argue that many advances fail to translate into practice because of two limiting assumptions. First, most approaches focus on a transductive learning setting which cannot handle unseen users or items and second, many existing methods are developed for static settings that cannot incorporate new data as it becomes available. We argue that these are largely impractical assumptions on real-world platforms where new user interactions happen in real time. In this survey paper, we formalize both concepts and contextualize recommender systems work from the last six years. We then discuss why and how future work should move towards inductive learning and incremental updates for recommendation model design and evaluation. In addition, we present best practices and fundamental open challenges for future research.
