Incorporating System-Level Objectives into Recommender Systems

One of the most essential parts of any recommender system is personalization-- how acceptable the recommendations are from the user's perspective. However, in many real-world applications, there are other stakeholders whose needs and interests should be taken into account. In this work, we define the problem of multistakeholder recommendation and we focus on finding algorithms for a special case where the recommender system itself is also a stakeholder. In addition, we will explore the idea of incremental incorporation of system-level objectives into recommender systems over time to tackle the existing problems in the optimization techniques which only look for optimizing the individual users' lists.Comment: arXiv admin note: text overlap with arXiv:1901.0755

Annual Report 2016-2017

The College of Computing and Digital Media has always prided itself on curriculum, creative work, and research that stays current with changes in our various fields of instruction. As we looked back on our 2016-17 academic year, the need to chronicle the breadth and excellence of this work became clear. We are pleased to share with you this annual report, our first, highlighting our accomplishments. Last year, we began offering three new graduate programs and two new certificate programs. We also planned six degree programs and three new certificate programs for implementation in the current academic year. CDM faculty were published more than 100 times, had their films screened more than 200 times, and participated in over two dozen exhibitions. Our students were recognized for their scholarly and creative work, and our alumni accomplished amazing things, from winning a Student Academy Award to receiving a Pulitzer. We are proud of all the work we have done together. One notable priority for us in 2016-17 was creating and strengthening relationships with industry—including expanding our footprint at Cinespace and developing the iD Lab—as well as with the community, through partnerships with the Chicago Housing Authority, Wabash Lights, and other nonprofit organizations. We look forward to continuing to provide innovative programs and spaces this academic year. Two areas in particular we’ve been watching closely are makerspaces and the “internet of things.” We’ve already made significant commitments to these areas through the creation of our 4,500 square foot makerspace, the Idea Realization Lab, and our new cyber-physical systems bachelor’s program and lab. We are excited to continue providing the opportunities, curriculum, and facilities to support our remarkable students. David MillerDean, College of Computing and Digital Mediahttps://via.library.depaul.edu/cdmannual/1000/thumbnail.jp

Popularity Bias in Recommendation: A Multi-stakeholder Perspective

Traditionally, especially in academic research in recommender systems, the focus has been solely on the satisfaction of the end-user. While user satisfaction has, indeed, been associated with the success of the business, it is not the only factor. In many recommendation domains, there are other stakeholders whose needs should be taken into account in the recommendation generation and evaluation. In this dissertation, I describe the notion of multi-stakeholder recommendation. In particular, I study one of the most important challenges in recommendation research, popularity bias, from a multi-stakeholder perspective since, as I show later in this dissertation, it impacts different stakeholders in a recommender system. Popularity bias is a well-known phenomenon in recommender systems where popular items are recommended even more frequently than their popularity would warrant, amplifying long-tail effects already present in many recommendation domains. Prior research has examined various approaches for mitigating popularity bias and enhancing the recommendation of long-tail items overall. The effectiveness of these approaches, however, has not been assessed in multi-stakeholder environments. In this dissertation, I study the impact of popularity bias in recommender systems from a multi-stakeholder perspective. In addition, I propose several algorithms each approaching the popularity bias mitigation from a different angle and compare their performances using several metrics with some other state-of-the-art approaches in the literature. I show that, often, the standard evaluation measures of popularity bias mitigation in the literature do not reflect the real picture of an algorithm's performance when it is evaluated from a multi-stakeholder point of view.Comment: PhD Dissertation in Information Science (University of Colorado Boulder