Life-Long Machine Learning for Recommender Systems

我们人类非常善于决策。在构建自动化决策方法时，包括数学家和人工智能工程师在内的计算机科学家从人类那里获得灵感。特别是，我们正在努力构建达到人类水平性能的计算机系统。如果这些方法成功，它们可以帮助人类做出更好的决策。这在我们面临大量选择的情况下，或者在我们不知道每个选择的可能结果的情况下，可能特别有用。例子包括决定对特定疾病的治疗选择，或者更平凡的决定，比如从一个大的图书目录中选择下一本书阅读，或者选择一组股票加入我们的金融投资组合。

We, humans, are very good at decision making. When building automated methods for decision making, computer scientists including mathematicians and engineers in artificial intelligence take inspiration from humans. In particular, we are trying to build computer systems that reach human-level performance. If such methods were successful they could assist humans in making even better decisions. This could be particularly useful in situations where we are faced with a a large number of choices or situations where we do not know about the possible outcomes of each choice. Examples includes decisions about treatment options for a particular medical condition or, more mundane ones, like choosing the next book to read from a large catalogue of books or a group of stocks to add to our financial portfolio.