In the framework of a collaboration with the Grenoble Informatics Laboratory (LIG), we are looking for a PhD candidate to join this fall on the topics of fairness in multi-stakeholder recommendation platforms. The student would be jointly supervised by Dr. Jean-Michel Renders from NAVER LABS Europe, by Dr. Sihem Amer-Yahia and Dr. Patrick Loiseau from the LIG.
Recommendation is a prominent machine learning task, used in a variety of platforms ranging from news aggregators to webtoons providers, ad publishers, online dating application, job marketplace, etc. At the heart of recommendation lies a ranking algorithm that ranks contents presented to a user. As recommendation platforms aﬀect users in many important ways, it is crucial to make them fair, but what is a fair ranking remains very unclear.
Algorithmic fairness has recently received great attention from the machine learning and data mining communities. A number of mathematical definitions of fairness have been proposed (demographic parity, equal opportunity, etc.) and researchers have proposed various solutions to build learning algorithms that respect those constraints. However, this line of work is currently limited in two directions. First, most of it considers classification whereas very little exists for ranking/recommendation (where it is arguably more complex to define/satisfy fairness). Second, it always considers one-sided fairness notions from the point of view of either content producers (e.g., news providers) or content consumers (e.g., users) in isolation. Recommendation platforms on the other hand act as mediators between these two actors and need to consider fairness notions from both points of view simultaneously. Naturally, whether a ranking is fair or not depends on the stakeholder’s perspective: intuitively, producers expect fairness in the exposure of their content objects while consumers expect fairness in the variety of items they are exposed to. These (possibly contradictory) objectives raise the crucial question of how to define fairness in multi-stakeholder recommendation settings and how to build algorithms that satisfy the defined notion.
In this PhD project, you will conduct research on fairness in multi-stakeholder recommendation platforms, with three main objectives. First, we will empirically study one such platform. We will do that on the example of the news and webtoons recommendation platforms of NAVER. We will work in particular on empirically quantifying unfairness. That will help us better understand the multi-stakeholder fairness issue from a data-driven perspective and to formalize the notions for this setting. Second, we will work on designing ranking algorithms that provide fair recommendation by design. This will involve theoretical work to prove that the designed algorithm satisfies the fairness properties identified. We will also work on characterizing the trade-oﬀ between the fairness of the diﬀerent stakeholders. Finally, we will test the algorithm in practice and design methods to audit the result so as to prove in practice to a third party that the algorithm respects the fairness properties. That involves in particular questions such as how to measure fairness, which data is needed to show that fairness is respected on a particular run, for how long, etc.
Throughout the PhD, we will work with the case of NAVER news recommendation platform in mind for motivation and for algorithms testing and empirical studies, but we will provide solutions that can readily apply to other similar platforms.
NAVER LABS Europe has full-time positions, PhD and PostDoc opportunities throughout the year which are advertised here and on international conference sites that we sponsor such as CVPR, ICCV, ICML, NeurIPS, EMNLP etc. NAVER LABS Europe is an equal opportunity employer.
LABS are in Grenoble in the French Alps. We have a multi and interdisciplinary approach to research with scientists in machine learning, computer vision, artificial intelligence, natural language processing, ethnography and UX working together to create next generation ambient intelligence technology and services that deeply understand users and their contexts.