91 citations · 248 across the 21 of their papers we have counts for
21 papers
Towards Defining, Measuring, and Leveraging Creativity in AI Applications
Jingyi Yang, Alexander Tuzhilin
Despite extensive research, computational creativity still lacks a conceptually grounded, practically relevant, and empirically applicable formal definition across contexts such as…
STARE: Predicting Decision Making Based on Spatio-Temporal Eye Movements
Moshe Unger, Alexander Tuzhilin, Michel Wedel
The present work proposes a Deep Learning architecture for the prediction of various consumer choice behaviors from time series of raw gaze or eye fixations on images of the decisi…
The Long Tail of Context: Does it Exist and Matter?
Konstantin Bauman, Alexey Vasilev, Alexander Tuzhilin
Context has been an important topic in recommender systems over the past two decades. A standard representational approach to context assumes that contextual variables and their st…
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
Anton Vakhrushev, Alexander Ryzhkov, Maxim Savchenko +3
We present an AutoML system called LightAutoML developed for a large European financial services company and its ecosystem satisfying the set of idiosyncratic requirements that thi…
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Pan Li, Maofei Que, Zhichao Jiang +2
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. T…
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
Pan Li, Zhichao Jiang, Maofei Que +2
Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem by aggregating and transferring user preferences across multiple categor…
Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations
Pan Li, Alexander Tuzhilin
Cross domain recommender systems have been increasingly valuable for helping consumers identify useful items in different applications. However, existing cross-domain models typica…
Heterogeneous Demand Effects of Recommendation Strategies in a Mobile Application: Evidence from Econometric Models and Machine-Learning Instruments
Panagiotis Adamopoulos, Anindya Ghose, Alexander Tuzhilin
In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers' utility and demand levels for individual produ…
Noise-Resilient Automatic Interpretation of Holter ECG Recordings
Konstantin Egorov, Elena Sokolova, Manvel Avetisian +1
Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient. Its interpretation becomes a difficu…
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
Leyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov +2
We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem. In contrast to many modern deep learning techniques, we build o…
Latent Unexpected Recommendations
Pan Li, Alexander Tuzhilin
Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendati…
Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
Pavel Blinov, Manvel Avetisian, Vladimir Kokh +2
In this paper we study the problem of predicting clinical diagnoses from textual Electronic Health Records (EHR) data. We show the importance of this problem in medical community a…
Radiologist-level stroke classification on non-contrast CT scans with Deep U-Net
Manvel Avetisian, Vladimir Kokh, Alex Tuzhilin +1
Segmentation of ischemic stroke and intracranial hemorrhage on computed tomography is essential for investigation and treatment of stroke. In this paper, we modified the U-Net CNN…
CoLES: Contrastive Learning for Event Sequences with Self-Supervision
Dmitrii Babaev, Ivan Kireev, Nikita Ovsov +4
We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw…
E.T.-RNN: Applying Deep Learning to Credit Loan Applications
Dmitrii Babaev, Maxim Savchenko, Alexander Tuzhilin +1
In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational d…
DDTCDR: Deep Dual Transfer Cross Domain Recommendation
Pan Li, Alexander Tuzhilin
Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories. However, previously proposed cr…
Towards Controllable and Personalized Review Generation
Pan Li, Alexander Tuzhilin
In this paper, we propose a novel model RevGAN that automatically generates controllable and personalized user reviews based on the arbitrarily given sentimental and stylistic info…
Latent Multi-Criteria Ratings for Recommendations
Pan Li, Alexander Tuzhilin
Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However…
Latent Unexpected and Useful Recommendation
Pan Li, Alexander Tuzhilin
Providing unexpected recommendations is an important task for recommender systems. To do this, we need to start from the expectations of users and deviate from these expectations w…
YASENN: Explaining Neural Networks via Partitioning Activation Sequences
Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov +1
We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propo…
Beyond Movie Recommendations: Solving the Continuous Cold Start Problem in E-commerceRecommendations
Julia Kiseleva, Alexander Tuzhilin, Jaap Kamps +6
Many e-commerce websites use recommender systems or personalized rankers to personalize search results based on their previous interactions. However, a large fraction of users has…