Notes

A Recommender System refers to a system that is capable of predicting the future preference of a set of items for a user, and recommend the top items. One key reason why we need a recommender system in modern society is that people have too much options to use from due to the prevalence of Internet. In the past, people used to shop in a physical store, in which the items available are limited. For instance, the number of movies that can be placed in a Blockbuster store depends on the size of that store. By contrast, nowadays, the Internet allows people to access abundant resources online. Netflix, for example, has an enormous collection of movies. Although the amount of available information increased, a new problem arose as people had a hard time selecting the items they actually want to see. This is where the recommender system comes in.
Methods for building Recommender Systems :
There are two methods to construct a recommender system :
1. Content-based recommendation : The goal of a recommendation system is to predict the scores for unrated items of the users. The basic idea behind content filtering is that each item have some features x
2. Collaborative Filtering : The downside of the content filtering is that it needs the side information for each item. For example, the genres such as romance and action are the side information of movies. It is very expensive to have someone watch movies and add side information for every movie out there and every movie in the future. Moreover how can one possibly list out all the features of a movie? What if one want to add a new feature? Do we want to add the new feature to all the movies? Collaborative filtering solve this problem. To start with, let’s think of the opposite approach to content filtering. Namely, can we predict the features of movies based on user preference parameters?

Calulation : https://towardsdatascience.com/recommender-system-a1e4595fc0f0