Abstract: 

Recommendation systems have wide-spread applications in both industry and academia. With ever-increasing popularity of the Internet, the amount of information increases exponentially and users spend significant time and energy to select their relevant items. When a user wants to purchase a particular item, they go through various reviews on that item and make a number of comparisons. Finding relevant information from large-scale datasets is often a difficult and time-consuming process, and users like the system automatically takes into account their interests and shows only relevant information. Recommendation systems are rapidly becoming a powerful technology in e-commerce and business analytics applications. These systems help the users cope with the information overload to find their desired content in a reasonable time by providing personalized recommendations for them. Moreover, recommendation systems help the e-commerce businesses to generate further revenue through increased sales and improved customer satisfaction. RSs are now a major topic in computer science and considerable effort has been made in the last two decades to advance them. In this talk, I will provide a review of recent advances in the field of recommendation systems. I will also highlight various applications and evaluations metrics often used to assess performance of recommender algorithms.

Bio: 

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