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Dynamic recommendations considering changing user preferences


   Department of Computer Science

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

User (or consumer) preferences change over time. These changes can be sudden or gradual, long-term or short-term, one-off or re-occurring. This project will explore ways of modelling dynamic user preferences and incorporating the generated models in recommender systems of different types (e.g. in recommending social or professional connections, products or services to buy, audio-video content such as movies, text-based content such as news and research articles).

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You must have a good Bachelor's degree (2.1 or higher, or equivalent) or Master's degree in Computer Science, Mathematics or a similar relevant subject.

Experience with machine learning, recommender systems and programming in Python are essential. Knowledge of graph analysis, deep learning, graph embedding and stream mining techniques are desirable. Experience with reinforcement learning and agent technology would also be useful.


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