Apply Reinforcement Learning to Programme Recommendation
Many people think reinforcement learning is too advanced and too academic which cannot apply in real business cases. The fact is just an opposite and very easy to be adopted gradually. In the presentation, Icarus will share his experience how to apply it in program recommendation from scratch to deep reinforcement learning. Traditional recommendation algorithms are collaborative filtering and content filtering. Icarus will explain how to encode them in a deep reinforcement network and add another important feature set, impression to the neural network. Difficulties such as avoiding local maximums and tricks on how to apply on startup will also be shared.
TVB is the largest television company in Hong Kong and its service spreads to Southeast Asia. It has more than million of users watching every day. Icarus is a Data Scientist in TVB. He designs the workflow and drives different data science projects which include program recommendation for MyTvSuper and BigBigChannel. Previously, he worked in Jobable, a startup matching candidates and job posts. He worked closely with the marketing team and help raise funding for series A with collaborated analysis blogs and deep learning models.