5 papers
Robust Fusion of Semantic and Behavioural Signals for LLM Reranking in Personalised Search
Aleksandr V. Petrov, Nathan Stein, Erik Lybecker +4
Personalised search must satisfy query intent while incorporating user context and historical interactions. LLM-based cross-encoders provide a single reranking interface, but injec…
Do Sequential Recommendation Benchmarks Really Require Higher-Order Sequence Modelling?
Aleksandr V. Petrov, Praveen Chandar, Paul N. Bennett +2
Sequential recommenders increasingly use language-model architectures designed to capture complex, context-dependent interactions. Yet it remains unclear whether widely used benchm…
Fast Adversarial Attacks with Gradient Prediction
Kamil Ciosek, Aleksandr V. Petrov, Nicolò Felicioni +1
Generating adversarial examples at scale is a core primitive for robustness evaluation, adversarial training, and red-teaming, yet even "fast" attacks such as FGSM remain throughpu…
From IR to RecSys: Evaluating LLM-based Judges in Cranfield-style Recommendation Collections
Gustavo Penha, Aleksandr V. Petrov, Claudia Hauff +9
The Cranfield paradigm has long provided reliable, reproducible evaluation in ad hoc retrieval, and recent work has begun extending this framework to recommender systems. A recent…
LLMs for estimating positional bias in logged interaction data
Aleksandr V. Petrov, Michael Murtagh, Karthik Nagesh
Recommender and search systems commonly rely on Learning To Rank models trained on logged user interactions to order items by predicted relevance. However, such interaction data is…