de Rijke, Maarten

Click Models for Web Search

Chuklin, A., Markov, I., & de Rijke, M. (2015) · Synthesis Lectures on Information Concepts, Retrieval, and Services

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 398

Contrastive Learning Reduces Hallucination in Conversations

Sun, W., Shi, Z., Gao, S., Ren, P., De Rijke, M., & Ren, Z. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence

Speech and dialogue systemsTopic Modeling

Cited by 69

Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems

Huang, J., Oosterhuis, H., de Rijke, M., & van Hoof, H. (2020) · Fourteenth ACM Conference on Recommender Systems

Recommender Systems and TechniquesAdvanced Bandit Algorithms Research

Cited by 64

Learning to Tokenize for Generative Retrieval

Sun, W., Yan, L., Chen, Z., Wang, S., Zhu, H., Ren, P., Chen, Z., Yin, D., de Rijke, M., & Ren, Z. (2023) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 36

Bayesian Personalized Feature Interaction Selection for Factorization Machines

Chen, Y., Ren, P., Wang, Y., & de Rijke, M. (2019) · Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 26

Going Beyond Popularity and Positivity Bias: Correcting for Multifactorial Bias in Recommender Systems

Huang, J., Oosterhuis, H., Mansoury, M., van Hoof, H., & de Rijke, M. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 17

Generative Retrieval as Multi-Vector Dense Retrieval

Wu, S., Wei, W., Zhang, M., Chen, Z., Ma, J., Ren, Z., de Rijke, M., & Ren, P. (2024) · Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Advanced Image and Video Retrieval TechniquesInformation Retrieval and Search Behavior

Cited by 15

Message Passing for Complex Question Answering over Knowledge Graphs

Vakulenko, S., Fernandez Garcia, J. D., Polleres, A., de Rijke, M., & Cochez, M. (2019) · Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Topic ModelingAdvanced Graph Neural Networks

Cited by 14

DeepShop: A Benchmark for Deep Research Shopping Agents

Lyu, Y., Zhang, X., Yan, L., de Rijke, M., Ren, Z., & Chen, X. (2025) · arXiv

Consumer Behavior in Brand Consumption and IdentificationDigital Marketing and Social Media

Cited by 9

Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models

Abolghasemi, A., Azzopardi, L., Hashemi, S. H., de Rijke, M., & Verberne, S. (2024) · arXiv

Topic ModelingEthics and Social Impacts of AI

Cited by 2

Recent Advances in Generative Information Retrieval

Tang, Y., Zhang, R., Ren, Z., Guo, J., & de Rijke, M. (2024) · Lecture Notes in Computer Science

Information Retrieval and Search BehaviorTopic Modeling

Cited by 2

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models

Lyu, Y., Yan, L., Wang, S., Shi, H., Yin, D., Ren, P., Chen, Z., de Rijke, M., & Ren, Z. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1