Sequential Recommendation For Sustainable Gardening

Gathering researchers in recommender systems, sequential and reinforcement learning on the one hand, and in sustainable agriculture, ecology and biodiversity preservation on the other hand, for an ambitious mixed project.

We want 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.

We 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.