Recommendation systems are one of the most important tools that individuals and companies use in digital business. They help improve user experience and increase business sales.
Recommendation systems are based on analyzing users’ data and providing them with personalized recommendations based on what they are searching for or buying. These systems are used in many fields such as e-commerce, digital marketing, distance education, and others.
In e-commerce, recommendation systems are used to improve user experience and increase business sales. It makes recommendations to customers based on their past purchase history and preferences. These systems are also used to analyze users’ behavior and provide them with personalized recommendations.
In digital marketing, recommendation systems are used to improve user experience and increase business sales. They are used to analyze users’ behavior and provide them with personalized recommendations based on their interests and preferences.
In distance education, recommendation systems are used to improve user experience and improve the quality of education. It makes recommendations to students based on their previous study records and interests. These systems are also used to analyze students’ behavior and provide them with customized recommendations.
In addition, recommendation systems are key tools in the digital business customer relationship. They help improve user experience and increase customer satisfaction and loyalty. It also helps analyze customer behavior and provide them with personalized recommendations based on their interests and preferences.
We will present , 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.
References
1
https://towardsdatascience.com/introduction-to-recommender-systems-6c66cf15ada
2 https://developers.google.com/machine-learning/recommendation
