Modern recommender systems in action (I know what movie you will watch in Netflix)

Speaker:  Georgia Koutrika – Athens, Greece
Topic(s):  Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science

Abstract

Recommender systems provide advice on items that may be of interest to a user (e.g., movies, products, travel, and leisure activities) by learning user preferences and relationships between users and items, and they comprise a major part of Amazon, Google, Netflix and other web sites. Early recommender systems used simple computational or machine learning models to learn user preferences (content-based approaches) or to capture relationships between users or items (collaborative filtering approaches), and they used these models to make recommendations. Recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems. In this talk, we will present major developments (milestones) in the area of recommender systems (covering a variety of methods, from early item-to item collaborative filtering to matrix factorization and deep neural networks). Our tour will take us to industrial-scale recommender systems that have shaped and are shaping the future of the recommender systems area. 

About this Lecture

Number of Slides:  100
Duration:  120 minutes
Languages Available:  English, Greek
Last Updated: 

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