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Found 5 papers, 2 papers with code
Learning Rich Rankings
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.
MiCRO: Multi-interest Candidate Retrieval Online
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.
Choosing to Rank
Ranking data arises in a wide variety of application areas but remains difficult to model, learn from, and predict.
Improving pairwise comparison models using Empirical Bayes shrinkage
As such, different models of the comparison process lead to different shrinkage estimators.
Pairwise Choice Markov Chains
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.
