Life-Long Machine Learning for Recommender Systems

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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.