Online workshop talking about recommender systems and relation with digital business from a software point of view. We will generally talk about recommender systems are algorithms aimed at suggesting relevant items to users (items being movies to watch, text to read, products to buy or anything else depending on industries).
Recommender systems are really critical in some industries as they can generate a huge amount of income when they are efficient or also be a way to stand out significantly from competitors. In this workshop , we will go through different paradigms of recommender systems. For each of them, we will present how they work, describe their theoretical basis and discuss their strengths and weaknesses.
We are going to overview the two major paradigms of recommender systems : collaborative and content – based methods.
First, we will talk about Collaborative filtering methods for recommender systems and the relation with digital Business . Collaborative filtering are methods that are based solely on the past interactions recorded between users and items in order to produce new recommendations. These interactions are stored in the so-called “user-item interactions matrix”.
Then we will talk about , the main idea that rules collaborative methods are that these past user-item interactions are sufficient to detect similar users and/or similar items and make predictions based on these estimated proximities.
then, we’ll look at implementing collaborative filtering using examples, (data set) taken from movie evaluation sites, the difference between the methods of users and their use from another user, and how to suggest from the experience of the second user through the similarity between it and the second rays in the system.
After that, we will talk about Content based filtering methods , content based approaches use additional information about users and/or items.
The idea of content based methods is to try to build a model, based on the available “features”, that explain the observed user-item interactions and Description of object features.
We will talk about code for content-based filtering, considering the example of a movie recommendation system . Also, code for collaborative filtering
And finally, we’ll talk about hybrid filtering, which is a combination of collaborative filtering and content-based filtering.
