42 citations · 87 across the 11 of their papers we have counts for
11 papers
Stop Using the Wilcoxon Test: Myth, Misconception and Misuse in IR Research
Julián Urbano
In benchmarking of Information Retrieval systems, the Wilcoxon signed-rank test is often treated as a safer alternative to the t-test. This belief is fueled by textbooks and recomm…
Mitigating Mainstream Bias in Recommendation via Cost-sensitive Learning
Roger Zhe Li, Julián Urbano, Alan Hanjalic
Mainstream bias, where some users receive poor recommendations because their preferences are uncommon or simply because they are less active, is an important aspect to consider reg…
New Insights into Metric Optimization for Ranking-based Recommendation
Roger Zhe Li, Julián Urbano, Alan Hanjalic
Direct optimization of IR metrics has often been adopted as an approach to devise and develop ranking-based recommender systems. Most methods following this approach aim at optimiz…
Leave No User Behind: Towards Improving the Utility of Recommender Systems for Non-mainstream Users
Roger Zhe Li, Julián Urbano, Alan Hanjalic
In a collaborative-filtering recommendation scenario, biases in the data will likely propagate in the learned recommendations. In this paper we focus on the so-called mainstream bi…
Statistical Significance Testing in Information Retrieval: An Empirical Analysis of Type I, Type II and Type III Errors
Julián Urbano, Harlley Lima, Alan Hanjalic
Statistical significance testing is widely accepted as a means to assess how well a difference in effectiveness reflects an actual difference between systems, as opposed to random…
Are Nearby Neighbors Relatives?: Testing Deep Music Embeddings
Jaehun Kim, Julián Urbano, Cynthia C. S. Liem +1
Deep neural networks have frequently been used to directly learn representations useful for a given task from raw input data. In terms of overall performance metrics, machine learn…