Yayınlar

Hyperbolic embedding methods for collaborative filtering constrain all representations to the hyperboloid manifold, imposing a single geometry regardless of data characteristics. We introduce LightConeFM, which removes this constraint and allows embeddings to freely occupy any causal region of Lorentz-Minkowski space — timelike, lightlike, or spacelike — using only standard gradient descent without Riemannian optimization. Experiments on four real-world datasets reveal two consistent findings: unconstrained embeddings outperform their constrained counterparts on every dataset (up to +7.0% AUC), and the learned causal zone distribution predicts where hyperbolic geometry provides benefit over Euclidean alternatives.

Digital job platforms vary dramatically in their interaction design, from single-click applications to multi-action marketplaces, yet the behavioral consequences of these design choices remain poorly understood. We present a large-scale comparative analysis of two job platforms with contrasting paradigms: Kariyer.net (single-action, 1.67M interactions, 104K users) and OLX Jobs (8-action marketplace, 65.5M interactions, 3.3M users). Contrary to conventional design wisdom, we find that platforms with richer interaction possibilities are associated with significantly lower user efficiency and goal achievement.