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"Recommendation": models, code, and papers

Deep Residual 3D U-Net for Joint Segmentation and Texture Classification of Nodules in Lung

Jun 25, 2020
Alexandr G. Rassadin

In this work we present a method for lung nodules segmentation, their texture classification and subsequent follow-up recommendation from the CT image of lung. Our method consists of neural network model based on popular U-Net architecture family but modified for the joint nodule segmentation and its texture classification tasks and an ensemble-based model for the fol-low-up recommendation. This solution was evaluated within the LNDb 2020 medical imaging challenge and produced the best nodule segmentation result on the final leaderboard.

* ICIAR 2020: Image Analysis and Recognition pp 419-427 
* 10 pages, 5 figures, 2 tables, accepted for publication at ICIAR 2020(LNDb Grand Challenge) 

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Visualisation to Explain Personal Health Trends in Smart Homes

Sep 28, 2021
Glenn Forbes, Stewart Massie, Susan Craw

An ambient sensor network is installed in Smart Homes to identify low-level events taking place by residents, which are then analysed to generate a profile of activities of daily living. These profiles are compared to both the resident's typical profile and to known "risky" profiles to support recommendation of evidence-based interventions. Maintaining trust presents an XAI challenge because the recommendations are not easily interpretable. Trust in the system can be improved by making the decision-making process more transparent. We propose a visualisation workflow which presents the data in clear, colour-coded graphs.

* 5 pages, 3 figures, First International Workshop on eXplainable Artificial Intelligence in Healthcare 

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Detecting and Quantifying Malicious Activity with Simulation-based Inference

Oct 07, 2021
Andrew Gambardella, Bogdan State, Naeemullah Khan, Leo Tsourides, Philip H. S. Torr, Atılım Güneş Baydin

We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides numerous advantages over other techniques, including but not limited to providing a disentangled representation of how malicious users acted under a structured model, as well as allowing for the quantification of damage caused by malicious users. We show experiments in malicious user identification using a model of regular and malicious users interacting with a simple recommendation algorithm, and provide a novel simulation-based measure for quantifying the effects of a user or group of users on its dynamics.

* Short version, appeared at ICML workshop on Socially Responsible Machine Learning 2021 

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Matrix Approximation under Local Low-Rank Assumption

Jan 15, 2013
Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer

Matrix approximation is a common tool in machine learning for building accurate prediction models for recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is only locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show improvements in prediction accuracy in recommendation tasks.

* 3 pages, 2 figures, Workshop submission to the First International Conference on Learning Representations (ICLR) 

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Improving Scientific Article Visibility by Neural Title Simplification

Apr 05, 2019
Alexander Shvets

The rapidly growing amount of data that scientific content providers should deliver to a user makes them create effective recommendation tools. A title of an article is often the only shown element to attract people's attention. We offer an approach to automatic generating titles with various levels of informativeness to benefit from different categories of users. Statistics from ResearchGate used to bias train datasets and specially designed post-processing step applied to neural sequence-to-sequence models allow reaching the desired variety of simplified titles to gain a trade-off between the attractiveness and transparency of recommendation.

* Contribution to the Proceedings of the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019) as part of the 41th European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, April 14, 2019. CEUR Workshop Proceedings, CEUR-WS.org 2019. Keywords: Scientific Text Summarization, Machine Translation, Recommender Systems, Personalized Simplification 

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Position-Based Multiple-Play Bandits with Thompson Sampling

Sep 28, 2020
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont

Multiple-play bandits aim at displaying relevant items at relevant positions on a web page. We introduce a new bandit-based algorithm, PB-MHB, for online recommender systems which uses the Thompson sampling framework. This algorithm handles a display setting governed by the position-based model. Our sampling method does not require as input the probability of a user to look at a given position in the web page which is, in practice, very difficult to obtain. Experiments on simulated and real datasets show that our method, with fewer prior information, deliver better recommendations than state-of-the-art algorithms.


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What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review

May 03, 2022
Terne Sasha Thorn Jakobsen, Anna Rogers

Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has been mostly approached as an automated recommendation problem rather than as a matter where different stakeholders (area chairs, reviewers, authors) have accumulated experience worth taking into account. We present the results of the first survey of the NLP community, identifying common issues and perspectives on what factors should be considered by paper-reviewer matching systems. This study contributes actionable recommendations for improving future NLP conferences, and desiderata for interpretable peer review assignments.

* NAACL 2022 camera-ready Replacement note: formatting mistake on pages 4-5 

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Energy saving in smart homes based on consumer behaviour: A case study

Sep 18, 2015
Michael Zehnder, Holger Wache, Hans-Friedrich Witschel, Danilo Zanatta, Miguel Rodriguez

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system mines for frequent and periodic patterns in the event data provided by the Digitalstrom home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects an opportunities to save energy without decreasing the comfort level it sends a recommendation to the residents.

* To be presented on IEEE International Smart Cities Conference 2015 

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Freshness-Aware Thompson Sampling

Sep 29, 2014
Djallel Bouneffouf

To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user's content in CARS through the bandit problem. We introduce in this paper an algorithm named Freshness-Aware Thompson Sampling (FA-TS) that manages the recommendation of fresh document according to the user's risk of the situation. The intensive evaluation and the detailed analysis of the experimental results reveals several important discoveries in the exploration/exploitation (exr/exp) behaviour.

* 21st International Conference on Neural Information Processing. arXiv admin note: text overlap with arXiv:1409.7729 

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A multinomial probabilistic model for movie genre predictions

Mar 25, 2016
Eric Makita, Artem Lenskiy

This paper proposes a movie genre-prediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movie genre has many practical applications including complementing the items categories given by experts and providing a surprise effect in the recommendations given to a user. We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach. We achieved 70% prediction rate using only 15% of the whole set for training.

* 5 pages, 4 figures, 8th International Conference on Machine Learning and Computing, Hong Kong 

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