Multi-sourced Integrated Ranking with Exposure Fairness

Integrated ranking system is one of the critical components of industrial recommendation platforms. An integrated ranking system is expected to generate a mix of heterogeneous items from multiple upstream sources. Two main challenges need to be solved in this process, namely, (i) Utility-fairness tradeoff: an integrated ranking system is required to balance the overall platform’s utility and exposure fairness among different sources; (ii) Information utilization from upstream sources: each source sequence has been carefully arranged by its provider, so how to efficiently utilize the source sequential information is important and should be carefully considered by the integrated ranking system. Existing methods generally cannot address these two challenges well. In this paper, we propose an integrated ranking model called Multi-sourced Constrained Ranking (MSCRank). It is a dual RNN-based model managing the utility-fairness tradeoff with multi-task learning, and capturing information in source sequences with a novel MA-GRU cell. We compare MSCRank with various baselines on public and industrial datasets, and MSCRank achieves the state-of-the-art performance on both utility and fairness. Online A/B test further validates the effectiveness of MSCRank.