Publications

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. ∆-OPE: Off-Policy Estimation with Pairs of Policies.
   O. Jeunen and A. Ustimenko. RecSys ’24

2. Multi-Objective Recommendation via Multivariate Policy Learning.
   O. Jeunen, J. Mandav, I. Potapov, N. Agarwal, S. Vaid, W. Shi and A. Ustimenko. RecSys ’24

3. Optimal Baseline Corrections for Off-Policy Contextual Bandits. S. Gupta, O. Jeunen, H. Oosterhuis and M. de Rijke. RecSys ’24

4. Powerful A/B-Testing Metrics and Where to Find Them.
   O. Jeunen, S. Baweja, N. Pokharna and A. Ustimenko. RecSys ’24

5. On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation O. Jeunen, I. Potapov and A. Ustimenko. KDD ‘24.

6. Learning Metrics that Maximise Power for Accelerated A/B-Tests.
   O. Jeunen and A. Ustimenko. KDD ‘24 (ADS Track).

7. Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation.
   S. Saket, O. Jeunen and Md. D. Kalim. SIGIR ‘24 (SIRIP).

8. Learning-to-Rank with Nested Feedback.
   H. Sagtani, O. Jeunen and A. Ustimenko. ECIR ‘24.

9. Variance Reduction in Ratio Metrics for Efficient Online Experiments.
   S. Baweja, N. Pokharna, A. Ustimenko and O. Jeunen. ECIR ‘24 (Industry Track).

10. Ad-load Balancing via Off-policy Learning in a Content Marketplace.
    H. Sagtani, M. Jhawar, R. Mehrotra and O. Jeunen. WSDM ‘24.
    [arXiv, video]

11. On Gradient Boosted Decision Trees and Neural Rankers: A Case-Study on Short-Video Recommendations at ShareChat.
    O. Jeunen, H. Sagtani, H. Doi, R. Karimov, N. Pokharna, D. Kalim, A. Ustimenko, C. Green, R. Mehrotra and W. Shi. FIRE’23.
    [arXiv]

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

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

14. 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]

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

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

17. 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]

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

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

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

21. 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. CONSEQUENCES — 3nd Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems.
   O. Jeunen, H. Oosterhuis, Y. Saito, F. Vasile and Y. Wang. CONSEQUENCES ‘24 (RecSys Workshop).
   [webpage]

2. 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]

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

4. 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]

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

6. 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]

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

8. Practical Bandits: An Industry Perspective.
   B. van den Akker, O. Jeunen, Y. Li, B. London, Z. Nazari and D. Parekh. The WebConf’23, WSDM ‘24 (Tutorial)
   [recording, webpage, arXiv]

9. 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]

10. 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]

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

12. 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]

13. 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]

14. 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]

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

16. 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]

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

18. 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]

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

20. 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]

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

22. 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.