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Leveraging Query Sensitivity for Practical Private Web Search.

, , , and . Middleware Posters and Demos, page 5-6. ACM, (2016)

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Sélection de variables en apprentissage d'ordonnancement. Évaluation des SVM pondérés., , and . Document Numérique, 18 (1): 97-121 (2015)EMDKG: Improving Accuracy-Diversity Trade-Off in Recommendation with EM-based Model and Knowledge Graph Embedding., , , and . WI/IAT, page 17-24. ACM, (2021)Recommandation de séquences d'activités lors d'événements distribués., , , and . CORIA, ARIA, (2018)Dataset Shift Quantification for Credit Card Fraud Detection., , , , , , and . AIKE, page 97-100. IEEE, (2019)Multiple Clicks Model for Web Search of Multi-clickable Documents., , and . ICEIS (1), page 298-303. SciTePress, (2013)Multiple perspectives HMM-based feature engineering for credit card fraud detection., , , , , , and . SAC, page 1359-1361. ACM, (2019)Enhancing Recommendation Diversity using Determinantal Point Processes on Knowledge Graphs., , , and . SIGIR, page 2001-2004. ACM, (2020)Séparateurs à Vaste Marge pondérés en norme L2 pour la sélection de variables en apprentissage d'ordonnancement., , and . CORIA-CIFED, page 321-336. ARIA-GRCE, (2014)Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs., , , , , , and . CoRR, (2019)Predicting Query Difficulty in IR: Impact of Difficulty Definition., , and . KSE, page 1-6. IEEE, (2019)