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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.

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

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From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience (talk given at Recsys 2012)

il y a 11 ans • 1672 Vues

Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 workshop at ACM Recsys 2012

il y a 11 ans • 1246 Vues

Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback - slides of our ECML PKDD paper

il y a 11 ans • 1445 Vues

Domonkos Tikk

Présentations

From a toolkit of recommendation algorithms into a real business: the Gravity R&D experience (talk given at Recsys 2012)

Recommender Systems Evaluation: A 3D Benchmark - presented at RUE 2012 workshop at ACM Recsys 2012

Fast ALS-Based Tensor Factorization for Context-Aware Recommendation from Implicit Feedback - slides of our ECML PKDD paper

Slides from CARR 2012 WS - Enhancing Matrix Factorization Through Initialization for Implicit Feedback Databases

Context-aware similarities within the factorization framework - presented at CARR 2013

Big Data in Online Classifieds

Idomaar crowd rec_reference_fw

Tartalomgazdagítás (content enrichment)

Challenges Encountered by Scaling Up Recommendation Services at Gravity R&D

Neighbor methods vs matrix factorization - case studies of real-life recommendations (Gravity LSRS2015 RECSYS 2015)

Recommenders on video sharing portals - business and algorithmic aspects

Lessons learnt at building recommendation services at industry scale

Documents

General factorization framework for context-aware recommendations

J’aime

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

Carrots for Couch Potatoes: Improving recommendations by motivating the crowd

Context-aware preference modeling with factorization

The Magic Barrier of Recommender Systems - No Magic, Just Ratings

Context-aware similarities within the factorization framework (CaRR 2013 presentation)

Best Practices in Recommender System Challenges

An architecture for evaluating recommender systems in real world scenarios

Mendeley Suggest: Engineering a Personalised Article Recommender System