Sebastian Schelter

Carousel interfaces have been the de-facto standard for streaming media services for over a decade. Yet, there has been very little research into user behavior with such interfaces, which thus remains poorly understood. Due to this lack of empirical research, previous work has assumed that behavi...

Fair re-ranking aims to promote long-tail items and enhance diversity within groups in information retrieval. While previous research on online fairness-aware re-ranking has shown promising outcomes, our comprehensive evaluation of online fair re-ranking methods over 20 settings reveals significa...

Generative information retrieval (GenIR) consolidates retrieval into a single neural model that decodes document identifiers (docids) directly from queries. While this model-as-index paradigm offers architectural simplicity, it is poorly suited to dynamic document collections. Unlike modular syst...

Generative retrieval (GR) ranks documents by autoregressively generating document identifiers. Because many GR methods rely on trie-constrained beam search, they are vulnerable to early pruning of relevant prefixes under finite-beam decoding. Planning Ahead in Generative Retrieval (PAG) mitigates...

Previous query performance prediction (QPP) methods typically return a single scalar value and do not require the predicted values to approximate a specific IR evaluation measure, leading to drawbacks: (i) a single scalar is insufficient to accurately represent different IR measures, especially w...

CLAX is a JAX-based library that implements classic click models using modern gradient-based optimization. While neural click models have emerged over the past decade, complex click models based on probabilistic graphical models (PGMs) have not systematically adopted gradient-based optimization,...

Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation.CS researchers currently face two barriers: the lack of a unified framework for efficiently sharing contributions with the community, and the difficult...

We focus on smaller open-source LMs (2–7B parameters), which are attractive for practical deployment due to their lower computational cost and greater accessibility than frontier-scale models. We show that instruction-tuned models generate answers even when explicitly prompted to refuse when the...

Retrieval-augmented generation (RAG) enhances large language model (LLM) reasoning by retrieving external documents, but also opens up new attack surfaces. We study knowledge-base poisoning attacks in RAG, where an attacker injects malicious content into the retrieval corpus, which is then surfac...

Machine unlearning (MU) enables the removal of selected training data from models to address privacy compliance, security, and liability issues in recommender systems. Existing MU benchmarks poorly reflect real-world recommender settings: they focus primarily on collaborative filtering, assume un...