| | --- |
| | license: mit |
| | language: |
| | - en |
| | - da |
| | - zh |
| | tags: |
| | - Responsible AI |
| | - Recommender Systems |
| | pretty_name: HealthRec |
| | size_categories: |
| | - 100M<n<1B |
| | --- |
| | #### Investigating Recommender Systems from the Healthiness Perspective: Benchmarks, Warnings and Enhancement |
| |
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| | This is the resource of our work "Investigating Recommender Systems from the Healthiness Perspective: Benchmarks, Warnings and Enhancement" which aims to investigate recommender systems from the healthiness perspective. |
| |
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| | This resource includes: |
| |
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| | **Three constructed datasets** with healthiness-related information under "Benchmarks" path; |
| |
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| | **Two healthiness-related metrics** proposed to quantitatively evaluate the healthiness of recommendation results under "HealthinessMetrics" path. |
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| | **A novel framwork HealthRec**, which is a generic and model-agnostic framework that can be seamlessly integrated into existing models to promote healthy content dissemination, under "HealthRec_code" path. |
| | |
| | More details about these resources can be found in the "readme.md" files in each path. |
| | |
| | By providing this resource, we seek to raise awareness of potential risks associated with recommender systems and provide a foundation for building more responsible, healthiness-aware approaches. |
