Olfa Nasraoui

Olfa Nasraoui
University of Louisville | UL · Department of Computer Engineering and Computer Science

PhD. in Computer Engineering and Computer Science

About

244
Publications
61,191
Reads
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5,585
Citations
Introduction
Olfa Nasraoui currently works at the Department of Computer Engineering and Computer Science, University of Louisville. Olfa does research in Data Mining, Machine Learning, Web Mining, and Artificial Intelligence. Her current research focuses on Explainable machine learning in particular explainable recommender systems, as well as algorithmic bias and fair machine learning, and clustering heterogenous and multi-source data sets.
Additional affiliations
August 2004 - present
University of Louisville
Position
  • Professor (Full)
January 2000 - July 2004
The University of Memphis
Position
  • Professor (Assistant)

Publications

Publications (244)
Article
Full-text available
In this paper, we present a complete framework and findings in mining Web usage patterns from Web log files of a real Web site that has all the challenging aspects of real-life Web usage mining, including evolving user profiles and external data describing an ontology of the Web content. Even though the Web site under study is part of a nonprofit o...
Article
Full-text available
Most accurate recommender systems are black-box models, hiding the reasoning behind their recommendations. Yet explanations have been shown to increase the user's trust in the system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. This gap between accuracy and transparency...
Conference Paper
Full-text available
Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants, etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular...
Chapter
Full-text available
Driven by the explosive growth in available data and decreasing costs of computation, Deep Learning (DL) has found much of its fame in problems involving classification tasks which are considered supervised learning. Deep learning has also been widely used to learn richer and better data representations from big data, without relying too much on hu...
Conference Paper
Full-text available
Accurate model-based Collaborative Filtering (CF) approaches, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. Yet explanations have been shown to improve the transparency of a recommender system by justifying recommendation...
Article
Full-text available
Recent research in recommender systems has demonstrated the advantages of pairwise ranking in recommendation. In this work, we focus on the state-of-the-art pairwise ranking loss function, Bayesian Personalized Ranking (BPR), and aim to address two of its limitations, namely: (1) the lack of explainability and (2) exposure bias. We propose a recomm...
Conference Paper
User feedback results in different rating patterns due to the users' preferences, cognitive differences, and biases. However, little research has taken into account cognitive biases when building recommender systems. In this paper, we propose novel methods to take into account user polarization into matrix factorization-based recommendation systems...
Preprint
Full-text available
Recommender systems that rely on Black-box Machine Learning (ML) models generate recommendations without explaining their rationale. However, they are generally more accurate when compared to white-box models, which are transparent and scrutable. One such black-box model is Matrix Factor-ization, a State of the Art recommendation technique that is...
Preprint
Full-text available
Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predicti...
Article
Full-text available
Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content. In addition, there is a lack of methods that can explain their predictions using the content of recommended songs and only a few approaches can handle the item cold start problem. In this work, we...
Article
This paper shows that least-square estimation (mean calculation) in a reproducing kernel Hilbert space (RKHS) F corresponds to different M-estimators in the original space depending on the kernel function associated with F. In particular, we present a proof of the correspondence of mean estimation in an RKHS for the Gaussian kernel with robust esti...
Conference Paper
Full-text available
The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommendations. However, most existing studies do not consider the iterative behavior of the system where the closed feedback loop plays a...
Preprint
Full-text available
The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These AI/machine learning algorithms attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative consequences stemming from...
Preprint
Full-text available
The closed feedback loop in recommender systems is a common setting that can lead to different types of biases. Several studies have dealt with these biases by designing methods to mitigate their effect on the recommendations. However, most existing studies do not consider the iterative behavior of the system where the closed feedback loop plays a...
Article
Full-text available
Traditionally, machine learning algorithms relied on reliable labels from experts to build predictions. More recently however, algorithms have been receiving data from the general population in the form of labeling, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information, such as biased...
Conference Paper
Full-text available
The ability to determine whether a robot’s grasp has a high chance of failing, before it actually does, can save significant time and avoid failures by planning for re-grasping or changing the strategy for that special case. Machine Learning (ML) offers one way to learn to predict grasp failure from historic data consisting of a robot’s attempted g...
Preprint
Full-text available
What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknow...
Preprint
Autoencoders are a common building block of Deep Learning architectures, where they are mainly used for representation learning. They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately, like all black box machine learning models, they are unable to explain their outputs. He...
Article
Full-text available
Autoencoders are a common building block of Deep Learning archi-tectures, where they are mainly used for representation learning.They have also been successfully used in Collaborative Filtering (CF) recommender systems to predict missing ratings. Unfortunately,like all black box machine learning models, they are unable to ex-plain their outputs. He...
Article
Full-text available
Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortuna...
Preprint
Full-text available
State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction by learning from temporal sequences of user actions. Despite advances in deep learni...
Conference Paper
Full-text available
Early supervised machine learning (ML) algorithms have used reliable labels from experts to build predictions. But recently, these algorithms have been increasingly receiving data from the general population in the form of labels, annotations, etc. The result is that algorithms are subject to bias that is born from ingesting unchecked information,...
Chapter
Full-text available
Machine Learning (ML) models are increasingly being used in many sectors, ranging from health and education to justice and criminal investigation. Therefore, building a fair and transparent model which conveys the reasoning behind its predictions is of great importance. This chapter discusses the role of explanation mechanisms in building fair mach...
Article
Full-text available
Social networks, along with their “event” organization, planning, and sharing tools, play an important role in connecting and engaging individuals and groups. These online spaces thrive with multifaceted activities and interests which give rise to rich content and user interaction that often crossover to the world of events. For these reasons, the...
Conference Paper
Full-text available
Personalized recommender systems are commonly used to filter information in social media, and recommen- dations are derived by training machine learning algorithms on these data. It is thus important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We investigate how filtering and disc...
Conference Paper
Full-text available
Personalized recommender systems are becoming increasingly relevant and important in the study of polarization and bias, given their widespread use in filtering information spaces. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algor...
Article
Full-text available
The goal of this study is to develop a model that explains the relationship between micro-RNAs, transcription factors, and their co-target genes. This relationship was previously reported in gene regulatory loops associated with 24 hour (24h) and 7 day (7d) time periods following ischemia-reperfusion injury in a rat's retina. Using a model system o...
Data
Supporting and opposing loops at 24h and 7d. A total of four sheets included. Sheet names are suffixed with “24h” or “7d” to indicate the IR time point and prefixed with “supporting”, “opposing” to indicate pairs of miRNAs-TFs that are working together or against each other respectively. (XLSX)
Data
Top mediated loops for each class of loops at 24h and 7d. A total of two sheets included for 24h, and 7d respectively. Each sheet contains four additional tables listing the top five mediated loops in each class of mediated loops. (XLSX)
Data
Validated mediated loops 24h and 7d. Partial validation from miRWALK db. A total of six sheets included. Sheet names are suffixed with “24h” or “7d” to indicate the IR time point and prefixed with “MT”, “MM”, or “MTM” to indicate Mediation by TFs, mediation by miRNAs, and mediation by both TFs, and miRNAs respectively. (XLSX)
Data
Mediation result and classification of closed regulatory loops at 24h and 7d. A total of eight sheets included. Sheet names are suffixed with “24h” or “7d” to indicate the IR time point and prefixed with “MT”, “MM”, or “MTM” to indicate Mediation by TFs, mediation by miRNAs, and mediation by both TFs, and miRNAs respectively. Sheets “24h”, and “7d”...
Conference Paper
Full-text available
The enormous scale of the available information and products on the Internet has necessitated the development of algorithms that intermediate between options and human users. These algorithms do not select information at random, but attempt to provide the user with relevant information. In doing so, the algorithms may incur potential negative conse...
Article
We describe the construction of a bilingual (English-Russian /Russian-English) semantic network covering basic concepts of computing. To construct the semantic network, we used the Computing Curricular series created during 2000-2015 under the aegis of ACM and IEEE and the current standards of IT specialists training in Russia, as well as some othe...
Article
Full-text available
We describe a service-based approach that provides a natural language interface to legacy information systems, built on top of relational database management systems. The long term goal is to make data management and analysis accessible to a wider range of users for a diverse range of purposes and to simplify the decision making process. We present...
Conference Paper
Full-text available
Topic Models are statistical models that can be used for discovering the abstract “topics” that may occur in a text corpus, however they face dramatic challenges when coping with very sparse and yet topically diverse micro-blog posts such as tweets. In such streams, not only are the topics very diverse, but also the vocabulary is huge, making the s...
Conference Paper
Full-text available
We bring to the fore of the recommender system research community, an inconvenient truth about the current state of understanding how recommender system algorithms and humans influence one another, both computationally and cognitively. Unlike the great variety of supervised machine learning algorithms which traditionally rely on expert input labels...
Article
Full-text available
Early supervised machine learning algorithms have relied on reliable expert labels to build predictive models. However, the gates of data generation have recently been opened to a wider base of users who started participating increasingly with casual labeling, rating, annotating, etc. The increased online presence and participation of humans has le...
Conference Paper
Full-text available
Online third party marketplaces link buyers and sellers by providing a neutral platform for exchange. However, this requires buyers to assess the quality of goods without being able to handle or sample them. Recent research has proposed extending the warranting principle, an emerging theory of online interpersonal impression formation, to the judge...
Article
Full-text available
Abstract Refining city services is gradually being placed in the hands of the citizens, or, as in the case of IBM’s initiative, Blet’s build a planet of smarter cities^ (https:// www-03.ibm.com/press/us/en/pressrelease/35573.wss), at their fingertips. By reducing cost and gaining control in building smart transportation management systems, IBM prov...
Article
Full-text available
Background The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extrac...
Conference Paper
Full-text available
Explanations have been shown to increase the user's trust in recommendations in addition to providing other benefits such as scrutability, which is the ability to verify the validity of recommendations. Most explanation methods are designed for classical neighborhood-based Collaborative Filtering (CF) or rule-based methods. For the state of the art...
Article
Full-text available
We present a novel environment for knowledge modeling and visualization for domain ontology design based on metaknowledge representation (metalevel) that was also implemented within an ontological paradigm framework. Metaknowledge representation makes the visual ontology editor more adaptable to the user's individual preferences. This improved adap...
Presentation
Full-text available
Clustering algorithms and tips for success: choosing the right algorithm, distance measure, handling initialization, cluster result evaluation methods and some tips for python implementation. The last slide has a Clustering recipe concept map! Check it out!
Conference Paper
Full-text available
We describe a proposed universal design infrastructure that aims at promoting better opportunities for students with disabilities in STEM programs to understand multimedia teaching material. The Accessible Educational STEM Videos Project aims to transform learning and teaching for students with disabilities through integrating synchronized captione...
Article
Full-text available
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into parts and its prediction perf...
Chapter
From e-commerce to e-learning, recommendation systems have given birth to an important and thriving research niche and have been deployed in a variety of application areas over the last decade. In particular, in the technology-enhanced learning (TEL) field, recommendation systems have attracted increasing interest, especially with the rise of educa...
Conference Paper
Full-text available
In this paper, we describe our solution to the RecSys2014 challenge and results on the test set. We briefly describe some of the challenges, then describe the methodology which starts with feature extraction and construction using the provided tweet data, in combination with IMDB as an external source. Feature construction also involved computing s...
Conference Paper
In this paper, we describe our solution to the RecSys2014 challenge and results on the test set. We briefly describe some of the challenges, then describe the methodology which starts with feature extraction and construction using the provided tweet data, in combination with IMDB as an external source. Feature construction also involved computing s...
Article
Full-text available
Social media has recently emerged as an invaluable source of information for decision making. Social media information reflects the interests of virtual communities in a spontaneous and timely manner. The need to understand the massive streams of data generated by social media platforms, such as Twitter and Facebook, has motivated researchers to us...
Article
Full-text available
Although clustering is an unsupervised learning approach, most clustering algorithms require the setting of parameters (such as the number of clusters, minimum density or distance threshold) in advance to work properly. Moreover, discovering an appropriate set of clusters is a difficult task since clusters can have any shape, size and density and i...