Thesis of Lu Gan
In this thesis, we work on designing new recommender systems aligning with real scenarios. Traditional recommender systems have been witnessed its prosperity in different domains, e.g., e-commerce, movie and music online platforms, and social networks. Techniques proposed for accuracy-based recommendation algorithms perform comparatively well to prediction results precisely. However, with a wider use of recommender systems in healthcare, geo-location related domains where sensitive data should be carefully processed and used, employing merely accuracy-based metrics to measure how good a recommender system isfar from enough.
Recently fairness, diversity, interpretability and transparency of recommender systems have been brought up to evaluate a new generation of recommender systems. Fairness and diversity aim at providing users with a well-balanced result from recommender systems, reducing and avoiding bias effects and “filter bubbles” phenomenon. Interpretability and transparency aim at a more comprehensible system for users, which in turn will enhance the trust and ameliorate the acceptance of such systems with regards to sensitive data. Existing systems do not integrate all these aspects. We aim at providing fair, diverse and explainable recommender systems to the community, together with anevaluation framework to properly evaluate these aspects.
Advisor: Léa Laporte
Coadvisor: Diana Nurbakova
Codirection: Sylvie Calabretto