Machine Learning in Recommender Systems - Recombee
Recommender systems are one of the most successful and widespread application of machine learning technologies in business. You will learn basic machine learning algorithms that are used in recommender systems such as matrix factorization or association rules. We will discuss problems of proper evaluation of recommendation models generated by these algorithms both on offline data and online.
We will explain the cold start problem and how you can reduce it by employing attributes of items including deep learning embeddings. You can use deep learning extensively in recommender systems and we will demonstrate one very interesting use case of predicting next purchased item using modern convolutional and recurrent neural neural networks.
Recommender algorithms can be also combined into ensembles. We show how our AutoML optimizes algorithms for every different recommendation scenario.
Finally, we will talk about recommender systems based on reinforcement learning capable of optimizing long term goals and KPIs such as customer lifetime value.
Recombee team - Pavel Kordík, Ivan Povalyev and Radek Bartyzal are data scientists working on algorithms improving Recombee cloud recommender system which provides personalized recommendations to hundreds of companies all over the world. Recombee runs a research laboratory at Faculty of Information Technology, Czech Technical University in Prague providing a platform for researchers and talented students to work on state of the art algorithms and live data.
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