Research Vision

Relevant science.

Real impact.

Innovation is not a buzzword at Ahold Delhaize. It’s been part of our DNA since our founding 150 years ago. Own-branded products, self-service and low mark-up shopping were pioneered by our founders, Albert Heijn and the Delhaize brothers.

Today, our family of great local brands continues to improve the shopping experience for millions of customers by carrying out multiple Innovation projects applied to the business at any given moment.

Our Vision 

Together with our academic partners we are pioneering in making new scientific and technological discoveries (creating knowledge) ensuring the success of Ahold Delhaize and it's leading brands now and in the future (fueling business).

Scientific R&D

As part of these innovation efforts Ahold Delhaize has a dedicated team that focuses on innovation with a long term business impact, by collaborating with scientists.

The team operates guides the strategic partnerships we have with academia. In this way, scientists can work with real data and business knowledge, which increases scientific knowledge to be shared with fellow academics, students and society.

As a company, we work with bright minds that help us shape our future. It’s a win-win! 


The rapid advancements in AI and robotics provide us with significant opportunities to make everyday shopping even easier for our customers and develop new solutions for our warehouses and last-mile delivery. Working together with academic partners will enable Ahold Delhaize to shape a technology-driven world in a responsible way. It helps us become a frontrunner in AI research and development for retail and ultimately build capabilities that are scalable for the group.

Frans Muller

CEO

Understanding how we can best connect our customers to the food and products that they need, is a tough technological challenge. The science that we develop for this is immediately relevant. And the impact is real.

Maarten de Rijke, PhD

Vice president Personalization & Relevance (Senior Research Fellow)

For our company it’s important to have multiple perspectives: a short term one with direct business impact, a medium term to prepare for the future, and a long term one that enables us to forge that future. Successful collaborations with academia are critical for such endeavors.

Sven Schagen, PhD

Lead Datascience Albert Heijn

Our Achievements

  • Rolf Jagerman and Maarten de Rijke. Accelerated Convergence for Counterfactual Learning to Rank. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020. 
  • Wenqiang Lei, Xiangnan He, Maarten de Rijke, and Tat-Seng Chua. Conversational Recommendation: Formulation, Methods, and Evaluation. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020.
  • Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Dongxiao Yu, Jun Ma, Maarten de Rijke, and Xiuzhen Cheng. Meta Matrix Factorization for Federated Rating Predictions. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020.
  • Chuan Meng, Pengjie Ren, Zhumin Chen, Weiwei Sun, Zhaochun Ren, Zhaopeng Tu, and Maarten de Rijke. DukeNet: A Dual Knowledge Interaction Network for Knowledge-Grounded Conversation. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020.
  • Harrie Oosterhuis and Maarten de Rijke. Policy-Aware Unbiased Learning to Rank for Top-k Rankings. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Harrie Oosterhuis and Maarten de Rijke. Taking the Counterfactual Online: Efficient and Unbiased Online Evaluation for Ranking. In ICTIR 2020: The 6th ACM International Conference on the Theory of Information Retrieval. ACM, September 2020
  • Zhiqiang Pan, Fei Cai, Yanxiang Ling, and Maarten de Rijke. Rethinking Item Importance in Session-based Recommendation. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Zhiqiang Pan, Fei Cai, Yanxiang Ling, and Maarten de Rijke. An Intent-guided Collaborative Machine for Session-based Recommendation. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, and Maarten de Rijke. Conversations with Search Engines. arXiv preprint arXiv:2004.14162, April 2020
  • Anton Steenvoorden, Emanuele Di Gloria, Wanyu Chen, Pengjie Ren, and Maarten de Rijke. Attribute-aware Diversification for Sequential Recommendations. In AIIS: The SIGIR 2020 Workshop on Applied Interactive Information Systems. ACM, July 2020
  • Manos Tsagkias, Tracy Holloway King, Surya Kallumadi, Vanessa Murdock, and Maarten de Rijke. Challenges and Research Opportunities in eCommerce Search and Recommendations. SIGIR Forum, 54(1), June 2020
  • Svitlana Vakulenko, Evangelos Kanoulas, and Maarten de Rijke. An Analysis of Mixed Initiative and Collaboration in Information-Seeking Dialogues. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Ali Vardasbi, Maarten de Rijke, and Ilya Markov. Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Nikos Voskarides, Dan Li, Pengjie Ren, Evangelos Kanoulas, and Maarten de Rijke. Query Resolution for Conversational Search with Limited Supervision. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jian-Yun Nie, Jun Ma, and Maarten de Rijke. Coding Electronic Health Records with Adversarial Reinforcement Path Generation. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Maarten de Rijke, Haitian Chen, Min Zhang, and Shaoping Ma. Preference-based Evaluation Metrics for Web Image Search. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020
  • Xiaohui Xie, Jiaxin Mao, Yiqun Liu, and Maarten de Rijke. Modeling User Behavior for Vertical Search: Images, Apps and Products. In SIGIR 2020: 43rd international ACM SIGIR conference on Research and Development in Information Retrieval. ACM, July 2020