GitHub - antonismand/Personalized-News-Recommendation: Multi Armed Bandits implementation using the Yahoo! Front Page Today Module User Click Log Dataset
UCB Thompson Sampling E-greedy LinUCB with disjoint linear models The dataset contains 45,811,883 user visits to the Today Module. For each visit, both the user and each of the candidate articles are associated with a feature vector of dimension 6 (including a constant feature), constructed using a conjoint analysis with a bilinear model.
Machine Learning in the Bandit Setting: Algorithms, Evaluation, and Case Studies (CS Seminar Lecture Series)
Much of machine-learning research is about discovering patterns---building intelligent agents that learn to predict future accurately from historical data. While this paradigm has been extremely successful in numerous applications, complex real-world problems such as content recommendation on the Internet often require the agents to learn to act optimally through autonomous interaction with the world they live in, a problem known as reinforcement learning.
Machine Learning in the Bandit Setting Algorithms, Evaluation, and Case Studies Lihong Li Machine Learning Yahoo! Research SEWM ppt download
Machine Learning in the Bandit Setting Algorithms, Evaluation, and Case Studies Lihong Li Machine Learning Yahoo! Research SEWM 2012-05-25 SEWM2 ACTION Statistics, ML, DM, ... DATA KNOWLEDGE UTILITY MORE DATA Reinforcement Learning Outline Introduction Basic Solutions Advanced algorithms Advanced Offline Evaluation Conclusions 2012-05-25SEWM3 Yahoo-User Interaction 2012-05-25SEWM4 ads, news, ranking, ...