FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
CoRR(2024)
摘要
In pursuit of fairness and balanced development, recommender systems (RS)
often prioritize group fairness, ensuring that specific groups maintain a
minimum level of exposure over a given period. For example, RS platforms aim to
ensure adequate exposure for new providers or specific categories of items
according to their needs. Modern industry RS usually adopts a two-stage
pipeline: stage-1 (retrieval stage) retrieves hundreds of candidates from
millions of items distributed across various servers, and stage-2 (ranking
stage) focuses on presenting a small-size but accurate selection from items
chosen in stage-1. Existing efforts for ensuring amortized group exposures
focus on stage-2, however, stage-1 is also critical for the task. Without a
high-quality set of candidates, the stage-2 ranker cannot ensure the required
exposure of groups. Previous fairness-aware works designed for stage-2
typically require accessing and traversing all items. In stage-1, however,
millions of items are distributively stored in servers, making it infeasible to
traverse all of them. How to ensure group exposures in the distributed
retrieval process is a challenging question. To address this issue, we
introduce a model named FairSync, which transforms the problem into a
constrained distributed optimization problem. Specifically, FairSync resolves
the issue by moving it to the dual space, where a central node aggregates
historical fairness data into a vector and distributes it to all servers. To
trade off the efficiency and accuracy, the gradient descent technique is used
to periodically update the parameter of the dual vector. The experiment results
on two public recommender retrieval datasets showcased that FairSync
outperformed all the baselines, achieving the desired minimum level of
exposures while maintaining a high level of retrieval accuracy.
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