Fairness Aware Recommendations on Behance

Abstract

Traditionally, recommender systems strive to maximize the user acceptance of the recommendations, while more recently, diversity and serendipity have also been addressed. In two-sided platforms, the users can have two personas, consumers who would like relevant and diverse recommendations, and creators who would like to receive exposure for their creations. If the new creators do not get adequate exposure, they tend to leave the platform, and consequently, less content is generated, resulting in lower consumer satisfaction. We propose a re-ranking strategy that can be applied to the scored recommendation lists to improve exposure distribution across the creators (thereby improving the fairness), without unduly affecting the relevance of recommendations provided to the consumers. We also propose a different notion of diversity, which we call representative diversity, as opposed to dissimilarity based diversity, that captures level of interest of the consumer in different categories. We show that our method results in recommendations that have much higher level of fairness and representative diversity compared to the state-of-art recommendation strategies, without compromising the relevance score too much. Interestingly, higher diversity and fairness leads to increased user acceptance rate of the recommendations.