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Fairly Recommending with Social Attributes: a Flexible and Controllable Optimization Approach

EasyChair Preprint no. 13092

12 pagesDate: April 25, 2024


Item-side group fairness (IGF) requires a recommendation model to treat different item groups similarly, and has a crucial impact on information diffusion, consumption activity, and market equilibrium. Previous IGF notions only focus on the direct utility of the item exposures, i.e., the exposure numbers across different item groups. Nevertheless, the item exposures also facilitate utility gained from the neighboring users via social influence, called social utility, such as information sharing on the social media. To fill this gap, this paper introduces two social attribute-aware IGF metrics, which require similar user social attributes on the exposed items across the different item groups. In light of the trade-off between the direct utility and social utility, we formulate a new multi-objective optimization problem for training recommender models with flexible trade-off while ensuring controllable accuracy. To solve this problem, we develop a gradient-based optimization algorithm and theoretically show that the proposed algorithm can find Pareto optimal solutions with varying trade-off and guaranteed accuracy. Extensive experiments on two real-world datasets validate the effectiveness of our approach.

Keyphrases: fairness, Recommender System, social

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jinqiu Jin and Haoxuan Li and Fuli Feng and Sihao Ding and Peng Wu and Xiangnan He},
  title = {Fairly Recommending with Social Attributes: a Flexible and Controllable Optimization Approach},
  howpublished = {EasyChair Preprint no. 13092},

  year = {EasyChair, 2024}}
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