CuCo Lab conversations | Bereket A. Yilma (FSTM)
With the advent of digital media, the availability of art content has greatly expanded, making it increasingly challenging for individuals to discover and curate works that align with their personal preferences and taste. The task of providing accurate and personalized Visual Art (VA) recommendations is thus a complex one, requiring a deep understanding of the intricate interplay of multiple modalities such as images, textual descriptions, or other metadata. Furthermore, it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and cognitive reflections they may trigger in users. This talk, will focus on efficiently capturing the elements (i.e., latent semantic relationships) of visual art for deriving personalized recommendations. I will showcase our recent research that proposes a novel approach to design and study personalised VA Recommendations based on textual and visual feature learning techniques, as well as their combinations. I will present our findings from a small-scale and a large-scale user-centric evaluation of the quality of the recommendations highlighting how this research contributes to our understanding of delivering personalised content its implications and how it can benefit other domains where personalization has gained momentum.
- Dr. Bereket A. Yilma (FSTM)
- Date: Friday, 09.06.2023
- Place: Campus Belval, MSA room 2.230
- Webex: https://unilu.webex.com/unilu/j.php?MTID=mc2168dca4efdafb10e92ab9dd48d2e7d