4 papers · 1 filter
The Benefits of Diversity: Combining Comparisons and Ratings for Efficient Scoring
Julien Fageot, Matthias Grossglauser, Lê-Nguyên Hoang +2
Should humans be asked to evaluate entities individually or comparatively? This question has been the subject of long debates. In this work, we show that, interestingly, combining…
Recycling History: Efficient Recommendations from Contextual Dueling Bandits
Suryanarayana Sankagiri, Jalal Etesami, Pouria Fatemi +1
The contextual duelling bandit problem models adaptive recommender systems, where the algorithm presents a set of items to the user, and the user's choice reveals their preference.…
Measuring IIA Violations in Similarity Choices with Bayesian Models
Hugo Sales Corrêa, Suryanarayana Sankagiri, Daniel Ratton Figueiredo +1
Similarity choice data occur when humans make choices among alternatives based on their similarity to a target, e.g., in the context of information retrieval and in embedding learn…
Recommendations with Sparse Comparison Data: Provably Fast Convergence for Nonconvex Matrix Factorization
Suryanarayana Sankagiri, Jalal Etesami, Matthias Grossglauser
This paper provides a theoretical analysis of a new learning problem for recommender systems where users provide feedback by comparing pairs of items instead of rating them individ…