138 citations · 265 across the 17 of their papers we have counts for
6 papers · 1 filter
When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?
Yushun Dong, Jundong Li, Tobias Schnabel
In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the r…
Situating Recommender Systems in Practice: Towards Inductive Learning and Incremental Updates
Tobias Schnabel, Mengting Wan, Longqi Yang
With information systems becoming larger scale, recommendation systems are a topic of growing interest in machine learning research and industry. Even though progress on improving…
Where Do We Go From Here? Guidelines For Offline Recommender Evaluation
Tobias Schnabel
Various studies in recent years have pointed out large issues in the offline evaluation of recommender systems, making it difficult to assess whether true progress has been made. H…
EvalRS: a Rounded Evaluation of Recommender Systems
Jacopo Tagliabue, Federico Bianchi, Tobias Schnabel +4
Much of the complexity of Recommender Systems (RSs) comes from the fact that they are used as part of more complex applications and affect user experience through a varied range of…
Unbiased Learning-to-Rank with Biased Feedback
Thorsten Joachims, Adith Swaminathan, Tobias Schnabel
Implicit feedback (e.g., clicks, dwell times, etc.) is an abundant source of data in human-interactive systems. While implicit feedback has many advantages (e.g., it is inexpensive…
Unbiased Comparative Evaluation of Ranking Functions
Tobias Schnabel, Adith Swaminathan, Peter Frazier +1
Eliciting relevance judgments for ranking evaluation is labor-intensive and costly, motivating careful selection of which documents to judge. Unlike traditional approaches that mak…