963 citations · 2.4k across the 9 of their papers we have counts for
10 papers
Dynamic Product Image Generation and Recommendation at Scale for Personalized E-commerce
Ádám Tibor Czapp, Mátyás Jani, Bálint Domián +1
Coupling latent diffusion based image generation with contextual bandits enables the creation of eye-catching personalized product images at scale that was previously either imposs…
The Effect of Third Party Implementations on Reproducibility
Balázs Hidasi, Ádám Tibor Czapp
Reproducibility of recommender systems research has come under scrutiny during recent years. Along with works focusing on repeating experiments with certain algorithms, the researc…
Widespread Flaws in Offline Evaluation of Recommender Systems
Balázs Hidasi, Ádám Tibor Czapp
Even though offline evaluation is just an imperfect proxy of online performance -- due to the interactive nature of recommenders -- it will probably remain the primary way of evalu…
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks
Massimo Quadrana, Alexandros Karatzoglou, Balázs Hidasi +1
Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Network…
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
Balázs Hidasi, Alexandros Karatzoglou
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressiv…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
Session-based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas +1
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on…
General factorization framework for context-aware recommendations
Balázs Hidasi, Domonkos Tikk
Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention r…
Context-aware recommendations from implicit data via scalable tensor factorization
Balázs Hidasi, Domonkos Tikk
Albeit the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world application…
Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback
Balázs Hidasi, Domonkos Tikk
Albeit, the implicit feedback based recommendation problem - when only the user history is available but there are no ratings - is the most typical setting in real-world applicatio…