A Study of Fairness and Information Heterogeneity in Recommendation Systems

Abstract

Recommender systems are an integral and successful application of machine learning in e-commerce industry and in everyday lives of online users. Recommendation algorithms are used extensively for news, musics, books, point of interests, or travel recommendation as well as in many other domains. Although much focus has been paid on improving recommendation quality, however, some real-world aspects are not considered: How to ensure that top-n recommendations are fair and not biased due to any popularity boosting events, such as awards for movies or songs? How to recommend items to entities by explicitly considering information from heterogeneous sources. What is the best way to model sequential recommendation systems as heterogeneous context-aware design, and learning on-the- y from spatial, temporal and social contexts. Can we model attributes and heterogeneous relations in a heterogeneous information network? The goal of this thesis is to pave the way towards the next generation of realworld recommendation systems tackling fairness and information heterogeneity challenges to improve the user experience, while giving good recommendations. This thesis bridges techniques from recommendation and deep-learning techniques for representation learning by proposing novel techniques to address the above real-world problems. We focus on four directions: (1) model the e ect of popularity bias over time on the consumption of items, (2) model the heterogeneous information associated with sequential history of users and social links for sequential recommendation, (3) model the heterogeneous links and rich content of nodes in an academic heterogeneous information network, and (4) learn semantics using topic modeling for nodes based on their content and heterogeneous links in a heterogeneous information network