6 accepted papers
Modern recommender systems are typically trained offline and deployed with parameters held fixed between periodic refreshes, yet user behavior can evolve substantially during deployment. This can cause ranking utility to degrade over time and makes it difficult to provide formal guarantees about rec…
Recommender systems (RS) may suffer from feedback-driven exposure concentration, where repeated engagement optimization collapses exposure onto a narrow set of categories, reducing catalog coverage and degrading long-horizon learning. Existing methods are often post hoc and typically lack principled…
Recommendation systems (RS) play a crucial role in assisting decision-making but often suffer from either a lack of credibility or unfairness problems. A few recommendation models have endeavored to address the problem from only one aspect, and approaches to solving both problems remain to be explor…
Partial label learning (PLL) allows each instance to be annotated with a set of candidate labels, but only one is the ground-truth label. Although the state-of-the-art (SOTA) PLL models have shown competitive performance, they cannot get rid of the negative influence from the noisy false-positive la…
Although Recommender Systems (RS) have been well-developed for various fields of applications, they often suffer from a crisis of platform credibility with respect to RS confidence and fairness, which may drive users away, threatening the platform's long-term success. In recent years, some works hav…
Although deep partial label learning (deep PLL) classifiers have shown their competitive performance, they are heavily influenced by the noisy false-positive labels leading to poorer performance as the training progresses. Meanwhile, existing deep PLL research lacks theoretical guarantee on the anal…