Online Clustering of Bandits

We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies. We provide a sharp regret analysis of this algorithm in a standard stochastic noise setting, demonstrate its scalability properties, and prove its effectiveness on a number of artificial and real-world datasets. Our experiments show a significant increase in prediction performance over state-of-the-art methods for bandit problems.Comment: In E. Xing and T. Jebara (Eds.), Proceedings of 31st International Conference on Machine Learning, Journal of Machine Learning Research Workshop and Conference Proceedings, Vol.32 (JMLR W&CP-32), Beijing, China, Jun. 21-26, 2014 (ICML 2014), Submitted by Shuai Li (https://sites.google.com/site/shuailidotsli

Document Similarity of Czech Supreme Court Decisions

Retrieval of court decisions dealing with a similar legal matter is a prevalent task performed by lawyers as it is a part of a relevant decision-making practice review. In spite of the natural language processing methods that are currently available, this legal research is still mostly done through Boolean searches or by contextual retrieval. In this study, it is experimentally verified whether the doc2vec method together with cosine similarity, can automatically retrieve the Czech Supreme Court decisions dealing with a similar legal issue as a given decision. Furthermore, the limits and challenges of these methods and its application on the Czech Supreme Court decisions are discussed