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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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Aktuellste Beliebteste SlideShares

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

Vor 11 Jahren • 1445 Aufrufe

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

Vor 11 Jahren • 1246 Aufrufe

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

Vor 11 Jahren • 1672 Aufrufe

Domonkos Tikk

Lessons learnt at building recommendation services at industry scale

Recommenders on video sharing portals - business and algorithmic aspects

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

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

Tartalomgazdagítás (content enrichment)

Idomaar crowd rec_reference_fw

Big Data in Online Classifieds

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

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

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

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

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