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"Recommendation": models, code, and papers

Choosing on Sequences

Feb 28, 2022
Bhavook Bhardwaj, Siddharth Chatterjee

The standard economic model of choice assumes that a decision maker chooses from sets of alternatives. A new branch of literature has considered the problem of choosing from lists i.e. ordered sets. In this paper, we propose a new framework that considers choice from infinite sequences. Our framework provides a natural way to model decision making in settings where choice relies on a string of recommendations. We introduce three broad classes of choice rules in this framework. Our main result shows that bounded attention is due to the continuity of the choice functions with respect to a natural topology. We introduce some natural choice rules in this framework and provide their axiomatic characterizations. Finally, we introduce the notion of computability of a choice function using Turing machines and show that computable choice rules can be implemented by a finite automaton.

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Rank-1 Similarity Matrix Decomposition For Modeling Changes in Antivirus Consensus Through Time

Dec 28, 2021
Robert J. Joyce, Edward Raff, Charles Nicholas

Although groups of strongly correlated antivirus engines are known to exist, at present there is limited understanding of how or why these correlations came to be. Using a corpus of 25 million VirusTotal reports representing over a decade of antivirus scan data, we challenge prevailing wisdom that these correlations primarily originate from "first-order" interactions such as antivirus vendors copying the labels of leading vendors. We introduce the Temporal Rank-1 Similarity Matrix decomposition (R1SM-T) in order to investigate the origins of these correlations and to model how consensus amongst antivirus engines changes over time. We reveal that first-order interactions do not explain as much behavior in antivirus correlation as previously thought, and that the relationships between antivirus engines are highly volatile. We make recommendations on items in need of future study and consideration based on our findings.

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On Releasing Annotator-Level Labels and Information in Datasets

Oct 12, 2021
Vinodkumar Prabhakaran, Aida Mostafazadeh Davani, Mark Díaz

A common practice in building NLP datasets, especially using crowd-sourced annotations, involves obtaining multiple annotator judgements on the same data instances, which are then flattened to produce a single "ground truth" label or score, through majority voting, averaging, or adjudication. While these approaches may be appropriate in certain annotation tasks, such aggregations overlook the socially constructed nature of human perceptions that annotations for relatively more subjective tasks are meant to capture. In particular, systematic disagreements between annotators owing to their socio-cultural backgrounds and/or lived experiences are often obfuscated through such aggregations. In this paper, we empirically demonstrate that label aggregation may introduce representational biases of individual and group perspectives. Based on this finding, we propose a set of recommendations for increased utility and transparency of datasets for downstream use cases.

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Multi-task Learning with Metadata for Music Mood Classification

Oct 10, 2021
Rajnish Kumar, Manjeet Dahiya

Mood recognition is an important problem in music informatics and has key applications in music discovery and recommendation. These applications have become even more relevant with the rise of music streaming. Our work investigates the research question of whether we can leverage audio metadata such as artist and year, which is readily available, to improve the performance of mood classification models. To this end, we propose a multi-task learning approach in which a shared model is simultaneously trained for mood and metadata prediction tasks with the goal to learn richer representations. Experimentally, we demonstrate that applying our technique on the existing state-of-the-art convolutional neural networks for mood classification improves their performances consistently. We conduct experiments on multiple datasets and report that our approach can lead to improvements in the average precision metric by up to 8.7 points.

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Click-through Rate Prediction with Auto-Quantized Contrastive Learning

Sep 27, 2021
Yujie Pan, Jiangchao Yao, Bo Han, Kunyang Jia, Ya Zhang, Hongxia Yang

Click-through rate (CTR) prediction becomes indispensable in ubiquitous web recommendation applications. Nevertheless, the current methods are struggling under the cold-start scenarios where the user interactions are extremely sparse. We consider this problem as an automatic identification about whether the user behaviors are rich enough to capture the interests for prediction, and propose an Auto-Quantized Contrastive Learning (AQCL) loss to regularize the model. Different from previous methods, AQCL explores both the instance-instance and the instance-cluster similarity to robustify the latent representation, and automatically reduces the information loss to the active users due to the quantization. The proposed framework is agnostic to different model architectures and can be trained in an end-to-end fashion. Extensive results show that it consistently improves the current state-of-the-art CTR models.

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Case Level Counterfactual Reasoning in Process Mining

Feb 25, 2021
Mahnaz Sadat Qafari, Wil van der Aalst

Process mining is widely used to diagnose processes and uncover performance and compliance problems. It is also possible to see relations between different behavioral aspects, e.g., cases that deviate more at the beginning of the process tend to get delayed in the last part of the process. However, correlations do not necessarily reveal causalities. Moreover, standard process mining diagnostics do not indicate how to improve the process. This is the reason we advocate the use of \emph{structural equation models} and \emph{counterfactual reasoning}. We use results from causal inference and adapt these to be able to reason over event logs and process interventions. We have implemented the approach as a ProM plug-in and have evaluated it on several data sets. Our ProM plug-in produces recommendations that indicate how specific cases could have been handled differently to avoid a performance or compliance problem.

* 15 pages, 4 figures 

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Optimal Flexural Design of FRP-Reinforced Concrete Beams Using a Particle Swarm Optimizer

Jan 25, 2021
M. S. Innocente, Ll. Torres, X. Cahís, G. Barbeta, A. Catalán

The design of the cross-section of an FRP-reinforced concrete beam is an iterative process of estimating both its dimensions and the reinforcement ratio, followed by the check of the compliance of a number of strength and serviceability constraints. The process continues until a suitable solution is found. Since there are infinite solutions to the problem, it appears convenient to define some optimality criteria so as to measure the relative goodness of the different solutions. This paper intends to develop a preliminary least-cost section design model that follows the recommendations in the ACI 440.1 R-06, and uses a relatively new artificial intelligence technique called particle swarm optimization (PSO) to handle the optimization tasks. The latter is based on the intelligence that emerges from the low-level interactions among a number of relatively non-intelligent individuals within a population.

* Submitted to FRPRCS-8, University of Patras, Patras, Greece, July 16-18, 2007 

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A survey of algorithmic recourse: definitions, formulations, solutions, and prospects

Oct 08, 2020
Amir-Hossein Karimi, Gilles Barthe, Bernhard Schölkopf, Isabel Valera

Machine learning is increasingly used to inform decision-making in sensitive situations where decisions have consequential effects on individuals' lives. In these settings, in addition to requiring models to be accurate and robust, socially relevant values such as fairness, privacy, accountability, and explainability play an important role for the adoption and impact of said technologies. In this work, we focus on algorithmic recourse, which is concerned with providing explanations and recommendations to individuals who are unfavourably treated by automated decision-making systems. We first perform an extensive literature review, and align the efforts of many authors by presenting unified definitions, formulations, and solutions to recourse. Then, we provide an overview of the prospective research directions towards which the community may engage, challenging existing assumptions and making explicit connections to other ethical challenges such as security, privacy, and fairness.

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Document Network Projection in Pretrained Word Embedding Space

Jan 16, 2020
Antoine Gourru, Adrien Guille, Julien Velcin, Julien Jacques

We present Regularized Linear Embedding (RLE), a novel method that projects a collection of linked documents (e.g. citation network) into a pretrained word embedding space. In addition to the textual content, we leverage a matrix of pairwise similarities providing complementary information (e.g., the network proximity of two documents in a citation graph). We first build a simple word vector average for each document, and we use the similarities to alter this average representation. The document representations can help to solve many information retrieval tasks, such as recommendation, classification and clustering. We demonstrate that our approach outperforms or matches existing document network embedding methods on node classification and link prediction tasks. Furthermore, we show that it helps identifying relevant keywords to describe document classes.

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On Designing Machine Learning Models for Malicious Network Traffic Classification

Jul 10, 2019
Talha Ongun, Timothy Sakharaov, Simona Boboila, Alina Oprea, Tina Eliassi-Rad

Machine learning (ML) started to become widely deployed in cyber security settings for shortening the detection cycle of cyber attacks. To date, most ML-based systems are either proprietary or make specific choices of feature representations and machine learning models. The success of these techniques is difficult to assess as public benchmark datasets are currently unavailable. In this paper, we provide concrete guidelines and recommendations for using supervised ML in cyber security. As a case study, we consider the problem of botnet detection from network traffic data. Among our findings we highlight that: (1) feature representations should take into consideration attack characteristics; (2) ensemble models are well-suited to handle class imbalance; (3) the granularity of ground truth plays an important role in the success of these methods.

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