MSSTATSeries | Ep.2 | A Guide to Recommender Systems

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Speaker Warunya Mahaisawariya
MSc Data Science London School of Economics
Slide : https://drive.google.com/file/d/1LN8cX3wsme7jhHkxgtAjegOlAdxQlHNx/view?usp=sharing

A recommender system is an information filtering system that is capable of processing large amount of data to infer the relationship between an item and a user. The objective of a recommender system is to use the processed information to predict whether a particular user would prefer an item or not, based on the user’s profile. Such personalized recommender systems are used in various online domains, for example movies and music providers (YouTube, Spotify), ecommerce sales platforms (Amazon, Alibaba), feed recommenders for media platforms (Instagram, Tiktok) etc. These companies have a large volume of users and items information in their databases. In this era of Big Data where there are simply too many options to choose from, a highly effective recommender system is beneficial to both service providers and users where it reduces the transaction cost of users finding relevant items.

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