Machine (Deep) Learning and Its Applications to Hotel Search Problems
Hotels metasearch engines such as trivago serve millions of queries daily. One of the challenges facing such search engines is to predict the intention of users queries. For instance, when a user types “Hotels in Munich” we might want to recommend “Oktoberfest” as an additional search keyword. Recently, machine/deep learning learning methods have shown a great performance in several domains including natural language processing and computer vision. In this talk, I would like to present a framework based on word embedding and it aims to compute reasonable recommendations for our users. Next, I would like to talk about future work, where we will apply deep learning approaches to classify hotels to provide better search facilities.
Rami Al-Salman. Al-Salman received a B.S. and M.S. in computer engineering from Jordan University of Science and University (JUST) in 2008 and 2011, respectively and a PhD in computer science from Bremen University, Bremen, Germany in 2014. He is a data scientist and machine (deep) learning engineer at trivago, where he has been since 2015. In this role, he is developing the next generation of recommendations engines that involve heavily AI and machine learning techniques such as Deep Learning, ANN, NB, SVM etc that will allow to capture the intention of the users queries. He worked as researcher and machine learning engineer in 2014 for a space company called OHB in Bremen, Germany. His research interests are in artificial intelligence, machine learning, and natural language processing. He published several well cited articles.