Recommender system empowered by contextual information

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This proposal is a collaborative effort between IBM Watson Analytics and the Data Science Laboratory at Ryerson University to build a recommender system empowered by contextual information. The fundamental question that a recommender system aims to answer is: "what does the end-user really want?" If the end-user does not know the answer to this question, s/he would benefit from using a recommender system. Our industrial partner - IBM Watson Analytics - is one of the global leaders in the field of recommender systems. IBM Watson Analytics provides cognitive technology processing information by understanding natural language, generating hypothesis based on evidence and learning as it goes. The major deliverable of this proposal is a software prototype recommender system empowered by contextual information that is compatible with the IBM Watson Analytics platform. This project is solution oriented in the sense that its solves an important problem for the flagship artificial intelligence product of our industrial partner - IBM Watson Analytics. IBM Watson is a question answering computer system capable of answering questions posed in natural language. One of the challenges that comes with Watson is how to make the best recommendations based on a limited amount of data, knowing that better recommendations lead to higher user engagements. Up until now, Watson does not use or leverage contextual information. It is with this problem in mind that IBM and that Data Science Laboratory came up with the solution of designing a recommender system empowered by contextual information. The end product will be a software prototype recommender system empowered by contextual information that increases the level of engagement and acts as a catalyst in ultimately helping the end-user find what s/he really wants.