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Domonkos Tikk

Domonkos Tikk

92 Followers
13 SlideShares 0 Clipboards 92 Followers 48 Followings
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13 SlideShares 0 Clipboards 92 Followers 48 Followings

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.
Contact Details
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
From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience (talk given at Recsys 2012)
10 years ago • 1657 Views
Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 workshop at ACM Recsys 2012
10 years ago • 1245 Views
Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback - slides of our ECML PKDD paper
10 years ago • 1441 Views
Slides from CARR 2012 WS - Enhancing Matrix Factorization Through Initialization for Implicit Feedback Databases
10 years ago • 897 Views
Context-aware similarities within the factorization framework - presented at CARR 2013
9 years ago • 524 Views
Big Data in Online Classifieds
8 years ago • 2079 Views
Idomaar crowd rec_reference_fw
8 years ago • 1193 Views
Tartalomgazdagítás (content enrichment)
8 years ago • 674 Views
Challenges Encountered by Scaling Up Recommendation Services at Gravity R&D
7 years ago • 1669 Views
Neighbor methods vs matrix factorization - case studies of real-life recommendations (Gravity LSRS2015 RECSYS 2015)
7 years ago • 5786 Views
Recommenders on video sharing portals - business and algorithmic aspects
7 years ago • 917 Views
Lessons learnt at building recommendation services at industry scale
6 years ago • 3039 Views
Documents (1)
General factorization framework for context-aware recommendations
7 years ago • 360 Views
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
  • Activity
  • About

Presentations (12)
See all
From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience (talk given at Recsys 2012)
10 years ago • 1657 Views
Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 workshop at ACM Recsys 2012
10 years ago • 1245 Views
Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback - slides of our ECML PKDD paper
10 years ago • 1441 Views
Slides from CARR 2012 WS - Enhancing Matrix Factorization Through Initialization for Implicit Feedback Databases
10 years ago • 897 Views
Context-aware similarities within the factorization framework - presented at CARR 2013
9 years ago • 524 Views
Big Data in Online Classifieds
8 years ago • 2079 Views
Idomaar crowd rec_reference_fw
8 years ago • 1193 Views
Tartalomgazdagítás (content enrichment)
8 years ago • 674 Views
Challenges Encountered by Scaling Up Recommendation Services at Gravity R&D
7 years ago • 1669 Views
Neighbor methods vs matrix factorization - case studies of real-life recommendations (Gravity LSRS2015 RECSYS 2015)
7 years ago • 5786 Views
Recommenders on video sharing portals - business and algorithmic aspects
7 years ago • 917 Views
Lessons learnt at building recommendation services at industry scale
6 years ago • 3039 Views
Documents (1)
General factorization framework for context-aware recommendations
7 years ago • 360 Views
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.
Contact Details
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

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