Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11280-021-00946-8 | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Multi-stakeholder recommender systems, Long-tail recommendation, Multi-objective evolutionary optimization, P-fairness, Personalized diversity | Journal | 24 |
Issue | ISSN | Citations |
6 | 1386-145X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Naime Ranjbar Kermany | 1 | 0 | 1.01 |
| Weiliang Zhao | 2 | 0 | 0.34 |
| Jian Yang | 3 | 0 | 0.34 |
| Jia Wu | 4 | 0 | 0.34 |
| Luiz Pizzato | 5 | 3 | 1.88 |