Author: Ahmadou Wagne

  • Temporal Analysis of Session Clusters in Clickstream Data from a Price Comparison Platform

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    Ahmadou Wagne
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    Author: Luca Turin Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne Abstract Understanding user behaviour is essential for designing digital services, improving user experience, and optimising commercial outcomes. This thesis investigates how behaviour varies over time and across contexts on a major price comparison platform. Building on prior work that offered a static segmentation of sessions, it…

  • New Job Opening as Software & DevOps Engineer

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    Ahmadou Wagne
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    We are seeking a Software & DevOps Engineer (20h/week) to join our team. In this role, you’ll support the development of an LLM-based conversational system that integrates diverse data sources and tools to enable natural language interaction. You’ll collaborate closely with researchers and industry partners on real-world applications, contributing to data integration, system implementation, and…

  • RecSys Summer School 2024

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    Ahmadou Wagne
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    📢Highlights from the 2024 RecSys Summer School in Bari, Italy! 🌍From October 8 to 12, the 2024 RecSys Summer School took place as a pre-program of this year’s ACM RecSys Conference. Co-funded by SIGCHI via its conference development fund, the school gathered leading experts to explore the latest developments in recommender systems. One of the…

  • Publications @ RecSys 2024

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    Ahmadou Wagne
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    🌟 Proud moment for our lab! 🌟 Ahmadou Wagne, a PhD student at our lab, recently presented a poster at Hashtag#RecSys2024 on “What to compare? Towards understanding user sessions on price comparison platforms,” co-authored with Julia Neidhardt (TU Wien). The research sheds light on the diverse behaviors of users on price comparison platforms, identifying six distinct session…

  • Doctoral Symposium @ ACM RecSys 2024

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    Ahmadou Wagne
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    We are pleased to share that Thomas Kolb was accepted to the Doctoral Symposium at ACM RecSys 2024. Thomas presented his ongoing research on cross-domain recommender systems, particularly focusing on large language models (LLMs) and fairness aspects in recommendations. Congratulations, Thomas, on this important achievement and for contributing valuable insights to the field! Hashtag#RecSys2024 Hashtag#DoctoralSymposium…