Dynamic user interest modeling and prediction integrating query box embedding with graph neural networks
User interests are multi-peaked, vaguely bounded, and prone to temporal drift, yet most recommendation models still encode them as single points in a latent space, discarding coverage and uncertainty alike. This paper represents interest as a region rather than a location, and lets that region move as the interaction r...