The task of item recommendation is to select the best items for a user from a large catalogue of items. Item recommenders are commonly trained from implicit feedback which consists of past actions that are positive only. Core challenges of item recommendation are (1) how to formulate a training objective from implicit feedback and (2) how to efficiently train models over a large item catalogue. This article provides an overview of item recommendation, its unique characteristics and some common approaches. It starts with an introduction to the problem and discusses different training objectives. The main body deals with learning algorithms and presents sampling based algorithms for general recommenders and more efficient algorithms for dot product models. Finally, the application of item recommenders for retrieval tasks is discussed.

中文翻译：

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隐式反馈的项目建议

项目推荐的任务是从大量项目中为用户选择最佳项目。项目推荐者通常从隐式反馈中接受培训，隐式反馈仅由过去的积极行为组成。项目推荐的核心挑战是（1）如何根据隐式反馈制定培训目标，以及（2）如何在大型项目目录上有效地训练模型。本文概述了项目推荐，其独特特征和一些常用方法。首先介绍问题，然后讨论不同的培训目标。主体处理学习算法，并为一般推荐者提供基于采样的算法，为点积模型提供更有效的算法。最后，讨论了项目推荐器在检索任务中的应用。