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Modeling the Dynamics of Online Learning Activity

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People are increasingly relying on the Web and social media to find solutions to their problems in a wide range of domains. In an online setting, closely related problems often lead to the same characteristic learning pattern, in which people sharing these problems visit related pieces of information, perform almost identical sequences of queries or, more generally, take a series of similar actions.

In this talk, I will introduce a novel modeling framework for clustering continuous-time grouped streaming data, the hierarchical Dirichlet Hawkes process (HDHP), which allows us to automatically uncover a wide variety of learning patterns from detailed traces of online learning activity. Our model allows for efficient inference, which scales to millions of online actions taken by thousands of users. Experiments on real data gathered from Stack Overflow reveal that our framework can recover meaningful learning patterns in terms of both content and temporal dynamics, as well as track users’ interests over time.

This talk is part of the Machine Learning @ CUED series.

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