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cs.IR2026
TransRetrieval: Scaling Up Transformer-Based Retrieval for Industrial Recommendation
Zhifei Zheng, Yunfei Liu, Bin Liu +7
Applying scaling laws to recommendation retrieval is hindered by feature heterogeneity: naively stacking Transformer layers yields diminishing returns because heterogeneous fields…
cs.IR2024
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval
Chen Xu, Jun Xu, Yiming Ding +2
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…