Not logged in.

Contribution Details

Type Dissertation
Scope Discipline-based scholarship
Title Improving recommendation diversity and identifying cultural biases for personalized ranking in large networks
Organization Unit
  • Bibek Paudel
  • Abraham Bernstein
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Date 2018
Date Annual Report 2019
Abstract Text Personalized ranking and filtering algorithms, also known as recommender systems, form the backbone of many modern web applications. They are used to tailor and rank suggestions for users in search engines, e-commerce sites, social networks, and news aggregators. As such systems gain prevalence in people’s day-to-day lives, they also affect people’s behavior in several ways. Of the several concerns regarding these systems, the diversity of choices they offer to users is one of the important ones. Exposure to diverse items is considered important for many reasons: for improving user-experience by adding richness, novelty and variety, reducing polarization and helping improve political participation through exposure to diverse viewpoints. It is therefore important to investigate ways to make recommender algorithms serve more diverse content. In this thesis, we present three new recommender algorithms for increasing the diversity of suggestions. We also present a new method to detect biases in knowledge bases, which are often used as input data source by recommender systems. The first algorithm uses a local exploration of the user-item feedback graph to increase the long-tail diversity of items. Long-tail items form a bulk of many product catalogs but compared to the few popular items that dominate recommendation lists, they are not recommended often. Our random-walk based method of promoting such long-tail items results in both more accurate and more diverse recommendations. In the second algorithm, we use a probabilistic latent-factor model to differentiate between positive and negative items in recommender systems. We find that the state-of-the-art algorithms not only have more negative items at the top of their recommendations, they also have low diversity and coverage. The recommendations produced by our approach is able to put fewer negative items at the top, and are also more diverse. In the third strategy, we look into the problem of diversifying political content recommendation. We collected data from the popular social network Twitter and created datasets that can be used to study political content recommendations. Based on these datasets, we first develop a new method to identify the ideological positions of not just users and political elites, but also of web-content. Then we used the identified ideological positions to diversify the recommendations based on diversification strategies that can be specified by the service provider. Our method is able to correctly identify political ideologies and to diversify recommendation of political content. Finally, since knowledge bases are used as input in many systems including recommender algorithms, we investigate them for the presence of human-like biases related to gender and race. We develop a new method based on cultural dimensions that can identify such biases in knowledge bases. Using our approach, it is possible to develop methods that can learn unbiased representations from knowledge bases, which can then be used by recommender algorithms. With our work, we present new ways to diversify and de-bias the output of recommender systems and we hope this will enable them to better serve the diverse needs of our societies.
Other Identification Number merlin-id:19047
Export BibTeX