Leveraging Topic Relationships to Improve Recommendations

Dataiku is returning to SF to present two talks focused on solutions for making the most of your user data (but safely).Tentative Schedule:6:30pm: Pizza + Beer mingling7:00pm: TBD Data Scientist at Dataiku7:30pm: Leveraging Topic Relationships to Improve Recommendations by Imeh Williams, Senior Staff Data Scientist at UdemyTalk Abstracts:Leveraging Topic Relationships to Improve Recommendations by Imeh Williams, Senior Staff Data Scientist at Udemy:Recommendations play a critical role in fulfilling Udemy’s mission of improving lives through learning by exposing users to a catalog consisting of over 80,000 courses covering thousands of topics. Using click, enrollment, and consumption data from a user base of 24 million learners, we constructed a topic graph that represents relationships between topics. This structure provides flexible recommendations that allow us to span the exploit-explore continuum. Recommendations powered by the topic graph improved revenue and increased the diversity of user’s purchases. The success of the topic graph spawned additional applications in other parts of the organization.Speaker bios:Imeh Williams is a senior staff data scientist at Udemy where he works on improving the discovery experience (search and recommendations) using causal inference, statistical modeling, and machine learning. Before joining Udemy, Imeh served as the first data scientist at IXL Learning. Prior to becoming a data scientist, Imeh spent over a decade as a quantitative researcher studying education policy and youth development. Imeh holds a bachelor’s degree in computer science from Brown University and a PhD in Education Policy from Stanford University.
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