41 citations · 103 across the 11 of their papers we have counts for
11 papers
ERASE -- A Real-World Aligned Benchmark for Unlearning in Recommender Systems
Pierre Lubitzsch, Maarten de Rijke, Sebastian Schelter
Machine unlearning (MU) enables the removal of selected training data from trained models, to address privacy compliance, security, and liability issues in recommender systems. Exi…
Understanding Visual Saliency of Outlier Items in Product Search
Fatemeh Sarvi, Mohammad Aliannejadi, Sebastian Schelter +1
In two-sided marketplaces, items compete for user attention, which translates to revenue for suppliers. Item exposure, indicated by the amount of attention items receive in a ranki…
Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code!
Sebastian Schelter, Stefan Grafberger
Machine learning (ML) applications that learn from data are increasingly used to automate impactful decisions. Unfortunately, these applications often fall short of adequately mana…
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines"
Stefan Grafberger, Paul Groth, Sebastian Schelter
Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to the…
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
Bojan Karlaš, David Dao, Matteo Interlandi +4
Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which tra…
Efficiently Maintaining Next Basket Recommendations under Additions and Deletions of Baskets and Items
Benjamin Longxiang Wang, Sebastian Schelter
Recommender systems play an important role in helping people find information and make decisions in today's increasingly digitalized societies. However, the wide adoption of such m…