5 papers
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…
Ranking Items from Discrete Ratings: The Cost of Unknown User Thresholds
Oscar Villemaud, Suryanarayana Sankagiri, Matthias Grossglauser
Ranking items is a central task in many information retrieval and recommender systems. User input for the ranking task often comes in the form of ratings on a coarse discrete scale…
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…