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Found 17 papers, 9 papers with code
Deep Pareto Reinforcement Learning for Multi-Objective Recommender Systems
Optimizing multiple objectives simultaneously is an important task for recommendation platforms to improve their performance.
The Long Tail of Context: Does it Exist and Matter?
Most of the prior CARS papers following representational approach manually selected and considered only a few crucial contextual variables in an application, such as time, location, and company of a person.
LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
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 this ecosystem has for AutoML solutions.
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction
Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied.
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction
While several cross domain sequential recommendation models have been proposed to leverage information from a source domain to improve CTR predictions in a target domain, they did not take into account bidirectional latent relations of user preferences across source-target domain pairs.
Dual Metric Learning for Effective and Efficient Cross-Domain Recommendations
Furthermore, we combine the dual learning method with the metric learning approach, which allows us to significantly reduce the required common user overlap across the two domains and leads to even better cross-domain recommendation performance.
Noise-Resilient Automatic Interpretation of Holter ECG Recordings
Holter monitoring, a long-term ECG recording (24-hours and more), contains a large amount of valuable diagnostic information about the patient.
Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks
We introduce a simple autoencoder based on hyperbolic geometry for solving standard collaborative filtering problem.
Latent Unexpected Recommendations
Unexpected recommender system constitutes an important tool to tackle the problem of filter bubbles and user boredom, which aims at providing unexpected and satisfying recommendations to target users at the same time.
Predicting Clinical Diagnosis from Patients Electronic Health Records Using BERT-based Neural Networks
We show the importance of this problem in medical community and present comprehensive historical review of the problem and proposed methods.
CoLES: Contrastive Learning for Event Sequences with Self-Supervision
We address the problem of self-supervised learning on discrete event sequences generated by real-world users.
E.T.-RNN: Applying Deep Learning to Credit Loan Applications
In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods.
DDTCDR: Deep Dual Transfer Cross Domain Recommendation
Cross domain recommender systems have been increasingly valuable for helping consumers identify the most satisfying items from different categories.
Towards Controllable and Personalized Review Generation
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 information.
Latent Multi-Criteria Ratings for Recommendations
Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences.
Latent Unexpected and Useful Recommendation
Providing unexpected recommendations is an important task for recommender systems.
YASENN: Explaining Neural Networks via Partitioning Activation Sequences
We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations.
