Reference number : GC-2017-Q1-03373, amendment number : , agreement type : G, recipient legal name : Charlin, Laurent (HEC Montréal), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : 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. 
 
One particular approach in artificial intelligence research is to frame such problems as a recommender system . At their core, recommender systems find patterns in user behaviour to improve personalized experiences and understand the environment, or the context, that they are acting in. The simplest recommender system suggests items of interests to its users based on their past behaviour (for example the next book to read or person to date). 
 
While recommender systems are an important field of current research and are widely deployed (think of Google's search suggestions or Amazon's "Customers Who Bought This Item Also Bought") their capabilities are limited. Of crucial importance to humans is our ability to adapt our decisions based on how our preferences evolve through time and based on changes in the environment. For example, how will my plans for the day change if it starts raining. Similarly, we, humans can reason over longer periods of time. For example, how do I pick a university major based on my values and future employment opportunities. Recommender systems and artificial intelligence more broadly do not yet offer the same possibilities. In particular, the mathematical models used in recommender systems do not have the ability for life-long learning, that is to continuously model changing user preferences and context and, accordingly, to adapt its recommendations. 
 
This research project will develop and validate novel mathematical tools using machine learning methods for life-long learning in recommender systems. Specifically, my research will use and develop novel techniques in the fast-growing fields of deep learning and reinforcement learning (RL). Deep learning can provide more accurate mathematical models of user behaviour over time as well as incorporate different sources of information. RL provides methods for turning model insights into good (sequences of) decisions. 
 
This research will contribute to Canada's position as a leader in artificial intelligence and fuel downstream applications, such as automated health assistants, that can help Canadians in their daily lives., program purpose french : 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. 
 
One particular approach in artificial intelligence research is to frame such problems as a recommender system . At their core, recommender systems find patterns in user behaviour to improve personalized experiences and understand the environment, or the context, that they are acting in. The simplest recommender system suggests items of interests to its users based on their past behaviour (for example the next book to read or person to date). 
 
While recommender systems are an important field of current research and are widely deployed (think of Google's search suggestions or Amazon's "Customers Who Bought This Item Also Bought") their capabilities are limited. Of crucial importance to humans is our ability to adapt our decisions based on how our preferences evolve through time and based on changes in the environment. For example, how will my plans for the day change if it starts raining. Similarly, we, humans can reason over longer periods of time. For example, how do I pick a university major based on my values and future employment opportunities. Recommender systems and artificial intelligence more broadly do not yet offer the same possibilities. In particular, the mathematical models used in recommender systems do not have the ability for life-long learning, that is to continuously model changing user preferences and context and, accordingly, to adapt its recommendations. 
 
This research project will develop and validate novel mathematical tools using machine learning methods for life-long learning in recommender systems. Specifically, my research will use and develop novel techniques in the fast-growing fields of deep learning and reinforcement learning (RL). Deep learning can provide more accurate mathematical models of user behaviour over time as well as incorporate different sources of information. RL provides methods for turning model insights into good (sequences of) decisions. 
 
This research will contribute to Canada's position as a leader in artificial intelligence and fuel downstream applications, such as automated health assistants, that can help Canadians in their daily lives., agreement title english : Life-Long Machine Learning for Recommender Systems, agreement title french : Life-Long Machine Learning for Recommender Systems, agreement number : RGPIN, agreement value : 140000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada