activity
20162026
most citedOff-line vs. On-line Evaluation of Recommender Systems in Small E-commerce

16 citations · 33 across the 7 of their papers we have counts for

collaborators

8 papers

cs.IR2026★ 1 cited

Efficient Learning of Sparse Representations from Interactions

Vojtěch Vančura, Martin Spišák, Rodrigo Alves +1

Behavioral patterns captured in embeddings learned from interaction data are pivotal across various stages of production recommender systems. However, in the initial retrieval stag…

cs.IR2026

From Knots to Knobs: Towards Steerable Collaborative Filtering Using Sparse Autoencoders

Martin Spišák, Ladislav Peška, Petr Škoda +2

Sparse autoencoders (SAEs) have recently emerged as pivotal tools for introspection into large language models. SAEs can uncover high-quality, interpretable features at different l…

cs.MM2025

Evaluating Keyframe Layouts for Visual Known-Item Search in Homogeneous Collections

Bastian Jäckl, Jiří Kruchina, Lucas Joos +3

Multimodal deep-learning models power interactive video retrieval by ranking keyframes in response to textual queries. Despite these advances, users must still browse ranked candid…

cs.HC2024

Personalisation of d'Hondt's algorithm and its use in recommender ecosystems

Stepan Balcar, Ladislav Peska, Peter Vojtas

In the area of recommender systems, we are dealing with aggregations and potential of personalisation in ecosystems. Personalisation is based on separate aggregation models for eac…

cs.IR2023

Looks Can Be Deceiving: Linking User-Item Interactions and User's Propensity Towards Multi-Objective Recommendations

Patrik Dokoupil, Ladislav Peska, Ludovico Boratto

Multi-objective recommender systems (MORS) provide suggestions to users according to multiple (and possibly conflicting) goals. When a system optimizes its results at the individua…

cs.IR2018★ 16 cited

Off-line vs. On-line Evaluation of Recommender Systems in Small E-commerce

Ladislav Peska, Peter Vojtas

In this paper, we present our work towards comparing on-line and off-line evaluation metrics in the context of small e-commerce recommender systems. Recommending on small e-commerc…

cs.IR2017

Towards Recommender Systems for Police Photo Lineup

Ladislav Peska, Hana Trojanova

Photo lineups play a significant role in the eyewitness identification process. This method is used to provide evidence in the prosecution and subsequent conviction of suspects. Un…

cs.IR2016★ 16 cited

Using the Context of User Feedback in Recommender Systems

Ladislav Peska

Our work is generally focused on recommending for small or medium-sized e-commerce portals, where explicit feedback is absent and thus the usage of implicit feedback is necessary.…