1 code implementation • NeurIPS 2020 • Arjun Seshadri, Stephen Ragain, Johan Ugander
Although the foundations of ranking are well established, the ranking literature has primarily been focused on simple, unimodal models, e. g. the Mallows and Plackett-Luce models, that define distributions centered around a single total ordering.
no code implementations • 28 Oct 2022 • Frank Portman, Stephen Ragain, Ahmed El-Kishky
Providing personalized recommendations in an environment where items exhibit ephemerality and temporal relevancy (e. g. in social media) presents a few unique challenges: (1) inductively understanding ephemeral appeal for items in a setting where new items are created frequently, (2) adapting to trends within engagement patterns where items may undergo temporal shifts in relevance, (3) accurately modeling user preferences over this item space where users may express multiple interests.
no code implementations • 13 Sep 2018 • Stephen Ragain, Johan Ugander
Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict.
no code implementations • 24 Jul 2018 • Stephen Ragain, Alexander Peysakhovich, Johan Ugander
As such, different models of the comparison process lead to different shrinkage estimators.
1 code implementation • NeurIPS 2016 • Stephen Ragain, Johan Ugander
As datasets capturing human choices grow in richness and scale -- particularly in online domains -- there is an increasing need for choice models that escape traditional choice-theoretic axioms such as regularity, stochastic transitivity, and Luce's choice axiom.