Bal谩zs Hidasi presentations

Bal谩zs Hidasi

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I'm a machine learning researcher with a wide interest in the field of machine learning and data mining. My current focus topics are deep learning, recommender systems and collaborative filtering; earlier I worked on model based time series classification. Currently I am the Head of Data Mining and Research at Gravity R&D, a Budapest (Hungary) based recommendation service provider company. I lead a small team of data scientists and work on my own recommendation systems related research. I actively publish the results of my research at scientific conferences and journals. I am an active participant in the EU FP7 funded CrowdRec project that aims at developing the new generation of recomm...

recommender systems context-awareness machine learning data mining implicit feedback deep learning shifttree g茅pi tanul谩s factorization context 补诲补迟产谩苍测谩蝉锄补迟 tensor factorization matrix factorization 颈诲艖蝉辞谤辞蝉锄迟谩濒测辞锄谩蝉 d枚nt茅si fa session-based recommendation recurrent neural network tenzor faktoriz谩ci贸颈苍颈肠颈补濒颈锄谩濒谩蝉 kontextus kontextus vez茅relts茅g m谩trix faktoriz谩ci贸补箩谩苍濒贸谤别苍诲蝉锄别谤 item-to-item recommendation itals alternating least squares initialization classification decision tree e-commerce stable diffusion generative ai benchmarking reimplementating algorithms reproducibility evaluation recsys2023 recsys negative sampling loss function gated recurrent unit recurrent neural networks neural networks summer school tutorial feature extraction phd thesis startup safary preference modeling scalability continuous context overview crowdrec research kollaborat铆v sz疟r茅s time series

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Bal谩zs Hidasi

Egyedi term茅k kreat铆vok t枚meges gy谩rt谩sa generat铆v AI seg铆ts茅g茅vel

The Effect of Third Party Implementations on Reproducibility

GRU4Rec v2 - Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

Deep Learning in Recommender Systems - RecSys Summer School 2017

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

Context aware factorization methods for implicit feedback based recommendation problems (HUN)

Deep learning to the rescue - solving long standing problems of recommender systems

Deep learning: the future of recommendations

Context-aware preference modeling with factorization

Approximate modeling of continuous context in factorization algorithms (CaRR14 presentation)

Utilizing additional information in factorization methods (research overview, April 2014)

Az聽implicit聽aj谩nl谩si probl茅ma 茅s n茅h谩ny megold谩sa (BME TMIT szemin谩rium el艖ad谩s, 2012)

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

iTALS: implicit tensor factorization for context-aware recommendations (ECML/PKDD 2012 presentation)

Initialization of matrix factorization (CaRR 2012 presentation)

ShiftTree: model alap煤颈诲艖蝉辞谤-oszt谩lyoz贸 (VK 2009 el艖ad谩s)

ShiftTree: model alap煤颈诲艖蝉辞谤-oszt谩lyoz贸 (ML@BP el艖ad谩s, 2012)

ShiftTree: model based time series classifier (ECML/PKDD 2011 presentation)