| Work Package | WP5 |
| Host Institution | 🇳🇱 UTW — UNIVERSITEIT TWENTE |
| PhD Enrolment | UTW |
| Recruiting Participant | UTW |
| Duration | M9–M45 (36 months) |
Recommender systems are well-known information filtering systems that suggest items most relevant to a user. To our knowledge, there are none that suggest investments in sustainable technologies and businesses. This project will develop and deploy a recommender system to help financial institutions and their clients invest in sustainable technologies.
The project informs user groups about investment sustainability. Sustainability KPI mapping and evaluation are project deliverables. The recommender system's explainability is crucial. Thus, the recommendations will be tailored to multiple user classes with appropriate explanations and interpretations. The system recommends sustainable investments, monitors portfolio performance, and dynamically updates financial and sustainable KPIs.
| Institution | Supervisor | Start Month | Duration (months) | Activities |
|---|---|---|---|---|
| BIS | Rafael Schmidt | M21 | 18 | Contribute macro-economic datasets, ongoing projects as well as overall expertise in banking supervision |
| ARC | Prof. Ioannis Emiris | M39 | 6 | Applied industry-research, work on the technological aspects of recommender systems |
DC 4 UTW University of Twente M9 36
5
DC 4 UTW UTW Month 9 36 months D 5.1
A recommender system to re-orient investments towards more sustainable technologies and businesses (WP 5)
Objectives: Recommender systems are well-known information filtering systems that suggest items most relevant to a user. To our
knowledge, there are none that suggest investments in sustainable technologies and businesses. This project will develop and deploy a
recommender system to help financial institutions and their clients invest in sustainable technologies.
Expected Results: The project informs user groups about investment sustainability. Sustainability KPI mapping and evaluation are project
deliverables. The recommender system's explainability is crucial. Thus, the recommendations will be tailored to multiple user classes with
appropriate explanations and interpretations. The system recommends sustainable investments, monitors portfolio performance, and
dynamically updates financial and sustainable KPIs.
Planned secondments: BIS, Rafael Schmidt, M21, 18 months, contribute macro-economic datasets, ongoing projects as well as overall
expertise in banking supervision
ARC, Prof. Dr. Ioannis Emiris, M39, 6 months, applied industry-research, work on the technological aspects of recommender systems
Fellow Host institution PhD enrolment Start date Duration Deliverables
5
| Code | Name | WP | Due |
|---|---|---|---|
| D5.1 | Design report for recommender systems | WP5 | M24 |