期刊
WEB CONFERENCE 2020: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2020)
卷 -, 期 -, 页码 1194-1204出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3366423.3380196
关键词
Fair Recommendation; Two-Sided Markets; Fair Allocation; Maximin Share; Envy-Freeness
资金
- TCS Research Fellowship
- Google Ph.D. Fellowship Award
- European Research Council (ERC) Advanced Grant under the EU Horizon 2020 Framework Programme [789373]
We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One Good (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality.
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