Olivier Jeunen

Lead Decision Scientist @ ShareChat

Google Scholar, ResearchGate, DBLP, Semantic Scholar

Journals

  1. Scheduling on a Budget: Avoiding Stale Recommendations with Timely Updates.
    R. Verachtert, O. Jeunen and B. Goethals. Elsevier MLWA’23
    [DOI]

  2. Pessimistic Decision-Making for Recommender Systems.
    O. Jeunen and B. Goethals. ACM ToRS’22 (Special Issue on Highlights of RecSys ‘21)
    [ACM DL, code]

  3. Embarrassingly Shallow Auto-Encoders for Dynamic Collaborative Filtering.
    O. Jeunen, J. Van Balen and B. Goethals. Springer UMUI’22 (Special Issue on Dynamic Recommender Systems and User Modeling)
    [pdf, code]

Conferences

  1. A Probabilistic Position Bias Model for Short-Video Recommendation Feeds.
    O. Jeunen. RecSys’23.
    [ACM DL, arXiv, code]

  2. Off-Policy Learning-to-Bid with AuctionGym.
    O. Jeunen, S. Murphy and B. Allison. KDD’23 (ADS Track).
    [ACM DL, AuctionGym]

  3. Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders.
    O. Jeunen, C. M. Gilligan-Lee, R. Mehrotra and M. Lalmas. NeurIPS’22.
    [NeurIPS, arXiv, code, video]

  4. Pessimistic Reward Models for Off-Policy Learning in Recommendation.
    O. Jeunen and B. Goethals. RecSys’21 (Best Student Paper Award)
    [ACM DL, pdf, code]

  5. Top-K Contextual Bandits with Equity of Exposure.
    O. Jeunen and B. Goethals. RecSys’21
    [ACM DL, pdf, code]

  6. Closed-Form Models for Collaborative Filtering with Side-Information.
    O. Jeunen, J. Van Balen and B. Goethals. RecSys’20 (Late-Breaking-Result)
    [ACM DL, pdf, code]

  7. Joint Policy-Value Learning for Recommendation.
    O. Jeunen, D. Rohde, F. Vasile and M. Bompaire. KDD’20
    [ACM DL, pdf, code]

  8. Efficient Similarity Computation for Collaborative Filtering in Dynamic Environments.
    O. Jeunen, K. Verstrepen and B. Goethals. RecSys’19
    [ACM DL, pdf, code]

  9. Revisiting Offline Evaluation for Implicit-Feedback Recommender Systems.
    O. Jeunen. RecSys’19 (Doctoral Symposium)
    [ACM DL, pdf]

  10. A Machine Learning Approach for IEEE 802.11 Channel Allocation.
    O. Jeunen, P. Bosch, M. Van Herwegen, K. Van Doorselaer, N. Godman and S. Latré. CNSM’18
    [pdf]

Workshops, Tutorials, Demos & Others

  1. RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation.
    G. Bénédict, O. Jeunen, S. Papa, S. Barghav, D. Odijk and M. de Rijke. GenRec ‘23 (CIKM Workshop).
    [arXiv]

  2. Offline Recommender System Evaluation under Unobserved Confounding.
    O. Jeunen and B. London. CONSEQUENCES ‘23 (RecSys Workshop).
    [arXiv, code, video]

  3. Ad-load Balancing via Off-policy Learning in a Content Marketplace.
    H. Sagtani, M. Jhawar, R. Mehrotra and O. Jeunen. CONSEQUENCES ‘23 (RecSys Workshop).
    [arXiv, video]

  4. A Common Misassumption in Online Experiments with Machine Learning Models.
    O. Jeunen. SIGIR Forum, PERSPECTIVES ‘23 (RecSys Workshop).
    [arXiv, code, video]

  5. CONSEQUENCES — 2nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems.
    O. Jeunen, T. Joachims, H. Oosterhuis, Y. Saito, F. Vasile and Y. Wang. CONSEQUENCES ‘23 (RecSys Workshop).
    [webpage]

  6. A Probabilistic Position Bias Model for Short-Video Recommendation Feeds.
    O. Jeunen. ML4SM ‘23 (The WebConf Workshop).
    [video]

  7. Practical Bandits: An Industry Perspective.
    B. van den Akker, O. Jeunen, Y. Li, B. London, Z. Nazari and D. Parekh. The WebConf’23 (Tutorial)
    [recording, webpage, arXiv]

  8. A Probabilistic Framework to Learn Auction Mechanisms via Gradient Descent.
    O. Jeunen, L. Stavrogiannis, A. Sayedi and B. Allison. AI4WebAds’23 (AAAI Workshop)
    [pdf, video]

  9. CONSEQUENCES — Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems.
    O. Jeunen, T. Joachims, H. Oosterhuis, Y. Saito and F. Vasile. CONSEQUENCES ‘22 (RecSys Workshop).
    [ACM DL, webpage]

  10. Learning to Bid with AuctionGym.
    O. Jeunen, S. Murphy and B. Allison. AdKDD ‘22 (KDD Workshop, Best Paper Award).
    [pdf, code, video]

  11. Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders.
    O. Jeunen, C. M. Gilligan-Lee, R. Mehrotra and M. Lalmas. WHY’21 (NeurIPS Workshop)
    [pdf]

  12. Offline Evaluation of Reward-Optimizing Recommender Systems: The Case of Simulation. I. Aouali, A. Benhalloum, M. Bompaire, B. Heymann, O. Jeunen, D. Rohde, O. Sakhi and F. Vasile. SimuRec ‘21 (Recsys Workshop)
    [arXiv]

  13. Recommender Systems through the Lens of Decision Theory:
    Unifying Policy- and Vaue-based Aproaches to Recommendation.
    F. Vasile, D. Rohde, O. Jeunen, A. Benhalloum and O. Sakhi. WWW’21 (Tutorial)
    [ACM DL, webpage]

  14. An Empirical Evaluation of Doubly Robust Learning for Recommendation.
    O. Jeunen and B. Goethals. REVEAL’20 (RecSys Workshop)
    [pdf]

  15. A Gentle Introduction to Recommendation as Counterfactual Policy Learning.
    F. Vasile, D. Rohde, O.Jeunen and A. Benhalloum. UMAP’20 (Tutorial)
    [ACM DL, pdf, slides & notebooks, video]

  16. Three Methods for Training on Bandit Feedback.
    D. Mykhaylov, D. Rohde, F. Vasile, M. Bompaire and O. Jeunen. CausalML’19 (NeurIPS Workshop)
    [arXiv]

  17. Learning from Bandit Feedback: An Overview of the State-of-the-art.
    O. Jeunen, D. Mykhaylov, D. Rohde, F. Vasile, A. Gilotte and M. Bompaire. REVEAL’19 (RecSys Workshop)
    [arXiv]

  18. On the Value of Bandit Feedback for Offline Recommender System Evaluation.
    O. Jeunen, D. Rohde and F. Vasile. REVEAL’19 (RecSys Workshop)
    [arXiv, notebook]

  19. Interactive Evaluation of Recommender Systems with SNIPER - An Episode Mining Approach.
    S. Moens, O. Jeunen and B. Goethals. RecSys’19 (Demo)
    [ACM DL, pdf, code, video]

  20. Predicting Sequential User Behaviour with Session-based Recurrent Neural Networks.
    O. Jeunen and B. Goethals. WSDM Cup’19 (WSDM Workshop)
    [pdf, code]

  21. Fair Offline Evaluation Methodologies for Implicit-Feedback Recommender Systems with MNAR Data.
    O. Jeunen, K. Verstrepen and B. Goethals. REVEAL’18 (RecSys Workshop)
    [pdf]

Theses

  1. Offline Approaches to Recommendation with Online Success.
    PhD in Computer Science, 2021.
    [pdf]

  2. Data-Driven Frequency Planning in IEEE 802.11 Networks.
    MSc in Computer Science, 2017.