:: Talks

Abstract:  
Choosing the right product to consume is nowadays a challenging problem due to the growing number of products and services. While increasing the number of choices provides an opportunity for a user to find the products satisfying her personal needs, it may at the same time overwhelm her by providing too many choices. Recommender Systems tackle this problem by providing to a user personalized suggestions that can match her particular taste rather than the mainstream taste. The accuracy of recommender systems largely depends on three factors: the quality of the prediction algorithm, and the quantity and quality of available user preferences. While research in the field of recommender systems often concentrates on improving prediction algorithms, even the best algorithms will fail if they are fed poor quality preference data during training. Preference Elicitation in recommender systems aims to remedy this problem by focusing on obtaining better quality data that more aptly reflects a user’s preferences. In an attempt to do that, a preference elicitation strategy selects the best items to be presented to the user in order to acquire her preferences and hence improve the output of the recommender system. In this talk, I will focus on the different forms of preference elicitations in recommender systems in order to address a range of grand challenges and discuss the potential solutions that have shown to be effective in mitigating such challenges.

Biography:
Mehdi Elahi is an Associate Professor (with a permanent contract) at the University of Bergen, one of the leading research and educational institutes in Norway. With nearly 20000 students and thousands of professors and research scholars, the University of Bergen has been ranked top 200 worldwide, in both Times and QS International rankings 2020. Before joining this university, in 2014, Mehdi Elahi has obtained his Ph.D. degree in Computer Science and since then, he has published more than 70 peer-reviewed journal and conference publications. His current #citation is 1700+ and his H-index is 20. His research has been mainly focused on AI, Data Science, and Cognitive Science, with an emphasis on their potential industrial applications such as on Recommender Systems. He has also co-invented and co-coowned an AI-related US-patent. Mehdi Elahi has been involved in the authorship of several EU grant proposals such as the large-scale grants, recently funded with a budget of 30 Million Euro, where he will serve as WP Leader for 8 years. Before that, he has received prestigious research credits from giant IT industries (i.e., Amazon and Google). His research findings have been published in some of the most prestigious reference literature of the field (e.g., Recommender Systems Handbook). One of his journal articles has been the second most cited paper of a top Elsevier journal. He has organized International Data Challenges together with top companies (i.e., Spotify and XING).