Several citizen science projects engage with the public around pollinator species, typically requesting data (e.g. in the form of photorecords of different species tagged by place and date). While such projects help scientists collect data, these data are rarely fed back to the public in any meaningful manner. In this paper, we address this through a recommender system based on Matrix Factorization over a matrix of observed bumblebee-plant interactions derived from data submitted to a citizen science project BeeWatch. The system recommends pollinator-friendly plants for domestic gardens and takes into account both the fact that different bumblebee species exhibit differing preferences for flowers, and that plants flower at different times of the year. The goal is to attract a range of bumblebee species to a garden and to ensure that these species have sufficient food sources through the season.
|Title of host publication
|Proceedings of International Workshop on Citizens for Recommender Systems, CitRec 2017 - In Conjunction with ACMRecSys 2017
|Association for Computing Machinery (ACM)
|Print publication - 31 Aug 2017
|2017 International Workshop on Citizens for Recommender Systems, CitRec 2017 - In Conjunction with ACMRecSys 2017 - Como, Italy
Duration: 31 Aug 2017 → …
|ACM International Conference Proceeding Series
|2017 International Workshop on Citizens for Recommender Systems, CitRec 2017 - In Conjunction with ACMRecSys 2017
|31/08/17 → …
Bibliographical notePublisher Copyright:
© 2017 Association for Computing Machinery.
- Matrix factorization
- Planting advice
- Pollinator friendly plants
- Recommender systems