Add to ORKGModatta aims at giving users control over their data and marketers the opportunity to target users with the right campaigns. Therefore, this paper proposes the use of Deep Learning to find users’ representations, so their data is kept private, but they can still learn about their interests. A Recommender System for predicting new interests is built to create a more complete representation of the users, which allows Modatta to find the offers that are of their most interest. After implementing the targeting strategy, this study shows how to evaluate the effectiveness of a campaign that could be conducted by Modatta
In this article, we describe deep learning-based recommender systems. First, we introduce deep learn...
In this work we try to explore different ways of building recommender systems. We check on the basel...
open7siDepending on the Internet as the main source of information regarding all aspects of our life...
This project studies two Deep Learning approaches, aiming to learn representations using embe...
The Work Project recognized two Deep Learning approaches intended to learn embedding repr...
This work project intends to propose a privacy-based system to Modatta, a start-up focus...
A data processing system can generate associations between user interests and target users based on ...
With the proliferation of online information, recommender systems have shown to be an effective meth...
Various practitioners in building recommendation systems currently leverage deep learn- ing techniqu...
In this paper, we focus on the popularity prediction for marketer-generated content (MGC), which has...
In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging task. Incr...
The usage of Internet applications, such as social networking and e-commerce is increasing exponenti...
Abstract — Recommender systems are becoming an essential part of smart services. When building a new...
The growth of data in recent years has motivated the emergence of deep learning in many Computer S...
With greater penetration of online services, the use of recommender systems to predict users’ propen...
In this article, we describe deep learning-based recommender systems. First, we introduce deep learn...
In this work we try to explore different ways of building recommender systems. We check on the basel...
open7siDepending on the Internet as the main source of information regarding all aspects of our life...
This project studies two Deep Learning approaches, aiming to learn representations using embe...
The Work Project recognized two Deep Learning approaches intended to learn embedding repr...
This work project intends to propose a privacy-based system to Modatta, a start-up focus...
A data processing system can generate associations between user interests and target users based on ...
With the proliferation of online information, recommender systems have shown to be an effective meth...
Various practitioners in building recommendation systems currently leverage deep learn- ing techniqu...
In this paper, we focus on the popularity prediction for marketer-generated content (MGC), which has...
In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging task. Incr...
The usage of Internet applications, such as social networking and e-commerce is increasing exponenti...
Abstract — Recommender systems are becoming an essential part of smart services. When building a new...
The growth of data in recent years has motivated the emergence of deep learning in many Computer S...
With greater penetration of online services, the use of recommender systems to predict users’ propen...
In this article, we describe deep learning-based recommender systems. First, we introduce deep learn...
In this work we try to explore different ways of building recommender systems. We check on the basel...
open7siDepending on the Internet as the main source of information regarding all aspects of our life...