A fairness-aware multi-stakeholder recommender system
- Publisher:
- Springer Science and Business Media LLC
- Publication Type:
- Journal Article
- Citation:
- World Wide Web, 2021, 24, (6), pp. 1995-2018
- Issue Date:
- 2021-11-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| RanjbarKermany2021_Article_AFairness-awareMulti-stakehold.pdf | Published version | 2.66 MB |
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Traditional recommender systems mainly focus on the accuracy of recommendation, which lead to recommender systems reinforcing popular items and ignoring lesser-known items. There is increasing evidence that providing good recommendations of surprising items can lead to better user satisfaction. Users may be delightfully surprised if long-tail items are brought to them. Marketplaces need to keep providers satisfied by making sure that their items get enough exposure. In this work, we propose a fairness-aware multi-stakeholder recommender system that uses a multi-objective evolutionary algorithm to make a trade-off between provider coverage, long-tail inclusion, personalized diversity, and recommendation accuracy. Experimental results against real-world datasets show that the proposed method significantly improves the diversity of recommended items in a personalized matter and the coverage of providers with no or minor loss of accuracy.
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