Sequential Recommendation For Sustainable Gardening is an Action Exploratoire from research institute Inria.
Identification and sharing of good, sustainable agriculture practice is both a scientific and societal challenge. The high-scalability of recommender systems, coherently aggregating data from millions of actively engaged users and constantly benefiting from research in machine learning, suggests connecting this field to sustainable agriculture may answer this challenge with significant success. The goal of the project “Sequential Recommendation for Sustainable Gardening (SR4SG)” is three-fold:
1) to gather researchers in the fields of recommender systems, sequential and reinforcement learning on the one hand, and in sustainable agriculture, ecology and biodiversity preservation on the other hand, to form an ambitious mixed community working in close collaboration.
2) to create a crowdsourced platform of “participative science” to collect sequential observations and actions in everyone’s garden, that will enable users to receive constantly improving recommendations involving state of the art algorithms, and researchers to organize recommendation challenges and improve their understanding of sustainable agricultural practice at large.
3) to lay the theoretical foundations of sequential learning for sustainable gardening, identify the novel bottlenecks and engage the reinforcement learning community in the process of solving them. This project funds two year of engineer, one year of postdoc, and several workshops in order to make significant progress on these three ambitious points.