Kevin Labille

University of Arkansas | U of A · Department of Computer Science and Computer Engineering

Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation. In this paper, we study how to achieve user-side fairness in personalized recommendation. W...

Twitter sentiment classification has been widely investigated in recent years and it is today possible to accurately determine the class label of a single tweet through various approaches. Although it could open new horizons for business or research, Twitter sentiment quantification, which aims to predict the prevalence of the positive class and th...

Automating the detection of fake news is a chal- lenging problem for the research community due to the various degrees of falsified information and ways in which it can be classified. In this work, we present a Bidirectional Encoder Representations (BERT)-based machine learning model that captures linguistic and emotional features of a document to...

Cross-domain recommendations have long been studied in traditional recommender systems, especially to solve the cold-start problem. Although recent approaches to dynamic personalized recommendation have leveraged the power of contextual bandits to benefit from the exploitation-exploration paradigm, very few works have been conducted on cross-domain...

Sentiment analysis is a broad and expanding field that aims to extract and classify opinions from textual data. Lexicon-based approaches are based on the use of a sentiment lexicon, i.e., a list of words each mapped to a sentiment score, to rate the sentiment of a text chunk. Our work focuses on predicting stock price change using a sentiment lexic...

Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation. In this paper, we study how to achieve user-side fairness in personalized recommendation. W...

Although keeping data and records about turtles and tortoises is time-consuming and effortful, it is yet a very important task to accomplish for both hobbyists and professionals. Today, whilst few softwares allow one to keep chelonian records, they tend to be unhandy and costly for both the private keeper and institutions. To overcome these problem...

Recommender systems can utilize Linked Open Data (LOD) to overcome some challenges, such as the item cold start problem, as well as the problem of explaining the recommendation. There are several techniques in exploiting LOD in recommender systems; one approach, called Linked Data Semantic Distance (LDSD), considers nearby resources to be recommend...

This work presents a novel approach for automatically generating a sentiment lexicon. We employ an unsupervised learning approach using several probabilistic and information theoretic models. While most of the unsupervised approaches require a set of seed words to begin their work, our methods differ from these by using no a priori knowledge. In ad...

The use of Linked Open Data (LOD) has been explored in recommender systems in different ways, primarily through its graphical representation. The graph structure of LOD is utilized to measure inter-resource relatedness via their semantic distance in the graph. The intuition behind this approach is that the more connected resources are to each other...

Sentiment analysis aims to identify and categorize customer's opinion and judgments using either traditional supervised learning techniques or unsupervised approaches. Traditionally, Sentiment Analysis is performed using machine learning techniques such as a naive Bayes classification or support vector machines (SVM), or could make use of a sentime...

Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than...

CiteSeer x is a digital library for scientific publications written by Computer Science researchers. Users are able to retrieve relevant documents from the database by searching by author name and/or keyword queries. Users may also receive recommendations of papers they might want to read provided by an existing conceptual recommender system. This...

Over the last few years, human computer interaction has become an active research area, which releases people from inactive, inconvenient communication with machine. In this paper, an automotive infotainment system with intelligent applications is presented. The proposed system combines the interactive gesture control for controlling in-car applica...