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You searched for +publisher:"University of Louisville" +contributor:("Sanders, Scott"). Showing records 1 – 3 of 3 total matches.

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University of Louisville

1. Schuschke, Joshua Chase. #SayItLoud : securing racial & academic identities for African American students through social media.

Degree: MA, 2015, University of Louisville

The rise of Black Twitter as an online cultural phenomenon has garnered attention as a force in the African American community. The online social network is a space for cultural performance, discussion, and debate. Generally, social media has created spaces for online communities to congregate around shared experiences and interests. African American users of popular social media such as blogs, Facebook, and the aforementioned Twitter have used the affordances of these platforms as tools to convey and construct their racial identities. The performance of racial identity offline is often carried over to these online environments, and arguably vice versa. When African American users come into contact with other African Americans they are able to reaffirm or renegotiate their identity, which they may carry with them back to offline environments. One such offline environment where African American identity is challenged is within the educational system. The purpose of this thesis is to deconstruct the anti-intellectualism narrative toward African American students embodied by oppositional culture perspectives, and show how secure racial identities can potentially lead to positive educational outcomes through social media platform affordances. I propose a model of online racial identity construction using social identity theory (Tajfel, 1981) and Cross’ (1991) model of Nigrescence accompanied by a pedagogical guide that shows how social networking sites can have educational benefits for African American students. Advisors/Committee Members: Jones, Ricky L., Byrd, W. Carson, Best, Latrica, Sanders, Scott.

Subjects/Keywords: Race; Ethnicity and Post-Colonial Studies

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APA (6th Edition):

Schuschke, J. C. (2015). #SayItLoud : securing racial & academic identities for African American students through social media. (Masters Thesis). University of Louisville. Retrieved from 10.18297/etd/2136 ; https://ir.library.louisville.edu/etd/2136

Chicago Manual of Style (16th Edition):

Schuschke, Joshua Chase. “#SayItLoud : securing racial & academic identities for African American students through social media.” 2015. Masters Thesis, University of Louisville. Accessed February 20, 2019. 10.18297/etd/2136 ; https://ir.library.louisville.edu/etd/2136.

MLA Handbook (7th Edition):

Schuschke, Joshua Chase. “#SayItLoud : securing racial & academic identities for African American students through social media.” 2015. Web. 20 Feb 2019.

Vancouver:

Schuschke JC. #SayItLoud : securing racial & academic identities for African American students through social media. [Internet] [Masters thesis]. University of Louisville; 2015. [cited 2019 Feb 20]. Available from: 10.18297/etd/2136 ; https://ir.library.louisville.edu/etd/2136.

Council of Science Editors:

Schuschke JC. #SayItLoud : securing racial & academic identities for African American students through social media. [Masters Thesis]. University of Louisville; 2015. Available from: 10.18297/etd/2136 ; https://ir.library.louisville.edu/etd/2136


University of Louisville

2. Badami, Mahsa. Peeking into the other half of the glass : handling polarization in recommender systems.

Degree: PhD, 2017, University of Louisville

This dissertation is about filtering and discovering information online while using recommender systems. In the first part of our research, we study the phenomenon of polarization and its impact on filtering and discovering information. Polarization is a social phenomenon, with serious consequences, in real-life, particularly on social media. Thus it is important to understand how machine learning algorithms, especially recommender systems, behave in polarized environments. We study polarization within the context of the users' interactions with a space of items and how this affects recommender systems. We first formalize the concept of polarization based on item ratings and then relate it to the item reviews, when available. We then propose a domain independent data science pipeline to automatically detect polarization using the ratings rather than the properties, typically used to detect polarization, such as item's content or social network topology. We perform an extensive comparison of polarization measures on several benchmark data sets and show that our polarization detection framework can detect different degrees of polarization and outperforms existing measures in capturing an intuitive notion of polarization. We also investigate and uncover certain peculiar patterns that are characteristic of environments where polarization emerges: A machine learning algorithm finds it easier to learn discriminating models in polarized environments: The models will quickly learn to keep each user in the safety of their preferred viewpoint, essentially, giving rise to filter bubbles and making them easier to learn. After quantifying the extent of polarization in current recommender system benchmark data, we propose new counter-polarization approaches for existing collaborative filtering recommender systems, focusing particularly on the state of the art models based on Matrix Factorization. Our work represents an essential step toward the new research area concerned with quantifying, detecting and counteracting polarization in human-generated data and machine learning algorithms.We also make a theoretical analysis of how polarization affects learning latent factor models, and how counter-polarization affects these models. In the second part of our dissertation, we investigate the problem of discovering related information by recommendation of tags on social media micro-blogging platforms. Real-time micro-blogging services such as Twitter have recently witnessed exponential growth, with millions of active web users who generate billions of micro-posts to share information, opinions and personal viewpoints, daily. However, these posts are inherently noisy and unstructured because they could be in any format, hence making them difficult to organize for the purpose of retrieval of relevant information. One way to solve this problem is using hashtags, which are quickly becoming the standard approach for annotation of various information on social media, such that varied posts about the same or related topic are annotated with… Advisors/Committee Members: Nasraoui, Olfa, Frigui, Hichem, Frigui, Hichem, Yampolskiy, Roman, Altiparmak, Nihat, Sanders, Scott.

Subjects/Keywords: data mining; web mining; recommender system; polarization; Computer Engineering; Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Badami, M. (2017). Peeking into the other half of the glass : handling polarization in recommender systems. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/2693 ; https://ir.library.louisville.edu/etd/2693

Chicago Manual of Style (16th Edition):

Badami, Mahsa. “Peeking into the other half of the glass : handling polarization in recommender systems.” 2017. Doctoral Dissertation, University of Louisville. Accessed February 20, 2019. 10.18297/etd/2693 ; https://ir.library.louisville.edu/etd/2693.

MLA Handbook (7th Edition):

Badami, Mahsa. “Peeking into the other half of the glass : handling polarization in recommender systems.” 2017. Web. 20 Feb 2019.

Vancouver:

Badami M. Peeking into the other half of the glass : handling polarization in recommender systems. [Internet] [Doctoral dissertation]. University of Louisville; 2017. [cited 2019 Feb 20]. Available from: 10.18297/etd/2693 ; https://ir.library.louisville.edu/etd/2693.

Council of Science Editors:

Badami M. Peeking into the other half of the glass : handling polarization in recommender systems. [Doctoral Dissertation]. University of Louisville; 2017. Available from: 10.18297/etd/2693 ; https://ir.library.louisville.edu/etd/2693


University of Louisville

3. Abdollahi, Behnoush. Accurate and justifiable : new algorithms for explainable recommendations.

Degree: PhD, 2017, University of Louisville

Websites and online services thrive with large amounts of online information, products, and choices, that are available but exceedingly difficult to find and discover. This has prompted two major paradigms to help sift through information: information retrieval and recommender systems. The broad family of information retrieval techniques has given rise to the modern search engines which return relevant results, following a user's explicit query. The broad family of recommender systems, on the other hand, works in a more subtle manner, and do not require an explicit query to provide relevant results. Collaborative Filtering (CF) recommender systems are based on algorithms that provide suggestions to users, based on what they like and what other similar users like. Their strength lies in their ability to make serendipitous, social recommendations about what books to read, songs to listen to, movies to watch, courses to take, or generally any type of item to consume. Their strength is also that they can recommend items of any type or content because their focus is on modeling the preferences of the users rather than the content of the recommended items. Although recommender systems have made great strides over the last two decades, with significant algorithmic advances that have made them increasingly accurate in their predictions, they suffer from a few notorious weaknesses. These include the cold-start problem when new items or new users enter the system, and lack of interpretability and explainability in the case of powerful black-box predictors, such as the Singular Value Decomposition (SVD) family of recommenders, including, in particular, the popular Matrix Factorization (MF) techniques. Also, the absence of any explanations to justify their predictions can reduce the transparency of recommender systems and thus adversely impact the user's trust in them. In this work, we propose machine learning approaches for multi-domain Matrix Factorization (MF) recommender systems that can overcome the new user cold-start problem. We also propose new algorithms to generate explainable recommendations, using two state of the art models: Matrix Factorization (MF) and Restricted Boltzmann Machines (RBM). Our experiments, which were based on rigorous cross-validation on the MovieLens benchmark data set and on real user tests, confirmed that our proposed methods succeed in generating explainable recommendations without a major sacrifice in accuracy. Advisors/Committee Members: Nasraoui, Olfa, Altiparmak, Nihat, Altiparmak, Nihat, Lauf, Adrian, Sanders, Scott, Zurada, Jacek.

Subjects/Keywords: recommender systems; machine learning; explanation; interpretable models; web mining; Artificial Intelligence and Robotics; Computer Sciences; Other Computer Sciences

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Abdollahi, B. (2017). Accurate and justifiable : new algorithms for explainable recommendations. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/2744 ; https://ir.library.louisville.edu/etd/2744

Chicago Manual of Style (16th Edition):

Abdollahi, Behnoush. “Accurate and justifiable : new algorithms for explainable recommendations.” 2017. Doctoral Dissertation, University of Louisville. Accessed February 20, 2019. 10.18297/etd/2744 ; https://ir.library.louisville.edu/etd/2744.

MLA Handbook (7th Edition):

Abdollahi, Behnoush. “Accurate and justifiable : new algorithms for explainable recommendations.” 2017. Web. 20 Feb 2019.

Vancouver:

Abdollahi B. Accurate and justifiable : new algorithms for explainable recommendations. [Internet] [Doctoral dissertation]. University of Louisville; 2017. [cited 2019 Feb 20]. Available from: 10.18297/etd/2744 ; https://ir.library.louisville.edu/etd/2744.

Council of Science Editors:

Abdollahi B. Accurate and justifiable : new algorithms for explainable recommendations. [Doctoral Dissertation]. University of Louisville; 2017. Available from: 10.18297/etd/2744 ; https://ir.library.louisville.edu/etd/2744

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