Seminar on Internet Technologies (Winter 2018/2019): Difference between revisions

|The proliferation of mobile devices especially smart phones brings remarkable opportunities for the e-commerce development. Recommendation systems have been being widely used by almost all the e-commerce platforms to help consumers find their ideal products to purchase more quickly. Modern recommendation systems have become increasingly more complex compared to their early content-based and collaborative filtering versions. In this survey, we will cover recent advances in recommendation methods, focusing on matrix factorization, multi-armed bandits, and methods for blending recommendations. We will also describe evaluation techniques, and outline open issues and challenges. The ultimate goal of this tutorial is to present a toolkit of new recommendation methods in perspective to data-related problems, and highlight opportunities and new research paths for researchers and practitioners that work on problems in the intersection of recommendation systems and databases.

|Basic knowledge about recommender system and machine learning

| [https://dl.acm.org/citation.cfm?id=2789995][https://arxiv.org/abs/1801.02294]

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