Personal Information
Organization / Workplace
Hungary area Hungary
Occupation
CEO at Gravity R&D
Industry
Technology / Software / Internet
Website
www.yusp.com
About
Gravity R&D is a recommendation engine provider, using machine learning to personalize digital customer experiences for SMEs and enterprises.
The Budapest-based company has been focusing on data science since 2009, using machine learning and Big Data analytics to create personalized customer experiences for brands in various industries.
Gravity's products, Yusp and Yuspify, help clients deliver better brand experiences, drive revenue growth and improve customer satisfaction. The company's personalization solutions easily serves 35+ billion personalized recommendations per month.
Gravity is strong in R&D, and proud to have a data mining team active in the field of recommender systems.
Tags
recommender system
context-awareness
gravity r&d
matrix factorization
implicit feedback
case study
technology
collaborative filtering
tensor factorization
benchmarking
neural networks
deep learning
recommendation-as-a-service
algorithms
user experience
video sharing portals
neighbor methods
netflix prize
optimization
machine learning
scaling up
szövegbányászat
ajánlórendszer
text mining
linked open data
content enrichment
open source
idomaar
crowdrec
content based filtering
classified media
personalization
big data
item-2-item
cold start problem
similarity
initialization
recommender sytem
user
evaluation
business
See more
Presentations
(12)
See all
Likes
(8)
See all
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
Balázs Hidasi
•
6 years ago
Carrots for Couch Potatoes: Improving recommendations by motivating the crowd
Fabian Abel
•
7 years ago
Context-aware preference modeling with factorization
Balázs Hidasi
•
7 years ago
The Magic Barrier of Recommender Systems - No Magic, Just Ratings
Alan Said
•
8 years ago
Context-aware similarities within the factorization framework (CaRR 2013 presentation)
Balázs Hidasi
•
9 years ago
Best Practices in Recommender System Challenges
Alan Said
•
10 years ago
An architecture for evaluating recommender systems in real world scenarios
Manuel Blechschmidt
•
11 years ago
Mendeley Suggest: Engineering a Personalised Article Recommender System
Kris Jack
•
10 years ago
Presentations
(12)
See all
Likes
(8)
See all
Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations
Balázs Hidasi
•
6 years ago
Carrots for Couch Potatoes: Improving recommendations by motivating the crowd
Fabian Abel
•
7 years ago
Context-aware preference modeling with factorization
Balázs Hidasi
•
7 years ago
The Magic Barrier of Recommender Systems - No Magic, Just Ratings
Alan Said
•
8 years ago
Context-aware similarities within the factorization framework (CaRR 2013 presentation)
Balázs Hidasi
•
9 years ago
Best Practices in Recommender System Challenges
Alan Said
•
10 years ago
An architecture for evaluating recommender systems in real world scenarios
Manuel Blechschmidt
•
11 years ago
Mendeley Suggest: Engineering a Personalised Article Recommender System
Kris Jack
•
10 years ago
Personal Information
Organization / Workplace
Hungary area Hungary
Occupation
CEO at Gravity R&D
Industry
Technology / Software / Internet
Website
www.yusp.com
About
Gravity R&D is a recommendation engine provider, using machine learning to personalize digital customer experiences for SMEs and enterprises.
The Budapest-based company has been focusing on data science since 2009, using machine learning and Big Data analytics to create personalized customer experiences for brands in various industries.
Gravity's products, Yusp and Yuspify, help clients deliver better brand experiences, drive revenue growth and improve customer satisfaction. The company's personalization solutions easily serves 35+ billion personalized recommendations per month.
Gravity is strong in R&D, and proud to have a data mining team active in the field of recommender systems.
Tags
recommender system
context-awareness
gravity r&d
matrix factorization
implicit feedback
case study
technology
collaborative filtering
tensor factorization
benchmarking
neural networks
deep learning
recommendation-as-a-service
algorithms
user experience
video sharing portals
neighbor methods
netflix prize
optimization
machine learning
scaling up
szövegbányászat
ajánlórendszer
text mining
linked open data
content enrichment
open source
idomaar
crowdrec
content based filtering
classified media
personalization
big data
item-2-item
cold start problem
similarity
initialization
recommender sytem
user
evaluation
business
See more