Controlling Diversity at Inference: Guiding Diffusion Recommender Models with Targeted Category Preferences

G Han, W Kweon, M Kim, H Yu - arxiv preprint arxiv:2411.11240, 2024‏ - arxiv.org
Diversity control is an important task to alleviate bias amplification and filter bubble
problems. The desired degree of diversity may fluctuate based on users' daily moods or …

CF-KAN: Kolmogorov-Arnold network-based collaborative filtering to mitigate catastrophic forgetting in recommender systems

JD Park, KM Kim, WY Shin - arxiv preprint arxiv:2409.05878, 2024‏ - arxiv.org
Collaborative filtering (CF) remains essential in recommender systems, leveraging user--
item interactions to provide personalized recommendations. Meanwhile, a number of CF …